Browse all reports and posts organized by technology tag.
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
大模型推理 MTP (Multi-Token Prediction) 算法 Survey · 围绕 DeepSeek DSpark 的全景调研
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
大模型推理 MTP (Multi-Token Prediction) 算法 Survey · 围绕 DeepSeek DSpark 的全景调研
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
大模型推理 MTP (Multi-Token Prediction) 算法 Survey · 围绕 DeepSeek DSpark 的全景调研
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
大模型推理 MTP (Multi-Token Prediction) 算法 Survey · 围绕 DeepSeek DSpark 的全景调研
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
联盟首批意向成员 + 初创企业调研报告 · 含 306 家深度分析
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
联盟首批意向成员 + 初创企业调研报告 · 含 306 家深度分析
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
NVIDIA GPU / CPU / 网络全产品线规格总览 · 含 8 篇 Agent 深度调研模块 (GPU 微架构 / 互联 / 内存 / 网络 / DGX / 边缘计算 / 学术论文 / 数据手册)
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
AMD Advancing AI 2026 全栈深度调研 · EPYC Venice (Zen 6/6c, 256 核) / MI455X (CDNA 4, 2.61 PFLOPS FP8) / Helios 机架 / Ryzen AI Gorgon · 芯片 + 集群 + 路线图
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
3 份主报告 + 16 篇 CAM Agent 深挖 + 10 篇 URPC 专题文档 · CAM 通信加速库 v2 (8章) / URPC 统一远程过程调用 (11篇) / CAM v1 · 21K+ 分析行数
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
12 章统一分析 + 3 专题 (第一性原理 / 热路径 / KV-Cache 4 层 / 分布式 / Ascend Overlay / Perf Handbook) · 百万序列 Prefill 专题 12 章 · KV Cache 即 Buffer / 1M token = 1.15 GB / NIXL vs Mooncake
vLLM / SGLang / LMCache / Mooncake / Dynamo 五大框架的 Prefill-Decode 分离 + KV Cache 路由内部实现源码级拆解
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
Keynote + 19 篇论文逐篇深度解读后的跨论文战略综合 · 6 条主轴: 同步税 / 存储层级重定义 / P2P 转移 / Superchip 冲击 / 批判性转向 / 训练路线分叉
dspark MTP 算法对算力与总线系统行业的深度影响分析 · 算法设计者视角的范式推演
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
MoE 专家并行推理在 Sparse CLOS 网络上的效率与成本收益深度分析 · MegaScale / MixNet / UBEP / SpecMoE 多篇对比
2026 年 Generative Recommendation 最新研究热点调查报告 · 算法 + 系统 + 工业落地
2026 年 Generative Recommendation 最新研究热点调查报告 · 算法 + 系统 + 工业落地
2026 年 Generative Recommendation 最新研究热点调查报告 · 算法 + 系统 + 工业落地
2026 年 Generative Recommendation 最新研究热点调查报告 · 算法 + 系统 + 工业落地
2026 年 Generative Recommendation 最新研究热点调查报告 · 算法 + 系统 + 工业落地
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
Sparse Clos / SlimFly / Jupiter 等无阻塞组网技术的深度调研 · 来源: 论文 + 厂商 + 学术会议
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
2026H1 AI 超节点总线技术市场调研 · NVLink / UALink / PCIe 6 / 光互联 + 产业格局
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
超节点行业指标定义深度调研 · 制造商(NVIDIA/华为/Google) / 云商 / 学术 三视角 + 量化指标体系
大模型推理 MTP (Multi-Token Prediction) 算法 Survey · 围绕 DeepSeek DSpark 的全景调研
3DLS 论文深度分析报告 · 芯片 / 系统 / AI 推理架构 交叉视角
3DLS 论文深度分析报告 · 芯片 / 系统 / AI 推理架构 交叉视角
3DLS 论文深度分析报告 · 芯片 / 系统 / AI 推理架构 交叉视角
3DLS 论文深度分析报告 · 芯片 / 系统 / AI 推理架构 交叉视角
3DLS 论文深度分析报告 · 芯片 / 系统 / AI 推理架构 交叉视角
联盟首批意向成员 + 初创企业调研报告 · 含 306 家深度分析
联盟首批意向成员 + 初创企业调研报告 · 含 306 家深度分析
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
PD 分离架构下请求路由的源码级拆解 · SGLang / Mooncake / LMCache 内部队列生命周期
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
Groq Trillium 与 NVIDIA LPX 微架构 / 集群架构深度对比 · 芯片设计范式 · 互联拓扑
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
SpaceX 全版图深度分析 · 产品布局 / 财务数据 / 政府合同 / 技术迭代 · 中国太空经济对比 · AI 算力交叉视角
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
HBM4 三强竞赛 / CXL 3.0 商用 / DRAM 结构性变化 · SK海力士 Samsung Micron 全景对比 · 2026.7
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
vLLM vs SGLang 全景对比 · Benchmark / 架构演进 / PD Disagg / KV Cache 设计哲学 · 2025.7~2026.7
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
源码级深度分析索引 · 主报告 + 三视角分报告 + 交叉讨论 + 专题深潜 · MoE AllToAll / NVLink+RDMA / Low-Latency
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
47 篇深度分析报告（架构4篇 + 博客↔DeepGEMM 10篇 + 三向对比9篇 + DeepEP独立分析11篇 + 测试分析7篇 + 对称内存4篇 + V2弹性架构2篇）· 同步范式 Barrier→mbarrier FIFO / 通信模型 消息传递→Load-Store 对称直传
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
BaM claims 4.4×~21.8× cost advantage over DRAM-only systems. But this conclusion rests on $/GB — while the real bottleneck for these workloads is IOPS. When IOPS demand scales beyond the tested 10-SSD configuration, the cost curve may bend in ways the paper never measured.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
CXL/RDMA distributed memory pools upgrade memory from compute appendage to poolable network resource, but existing data analytics tooling (pandas/cuDF/Ray/Arrow) still assumes memory is a local compute-node appendage. This architectural mismatch demands redesign at every layer — from UCX communication to Arrow zero-copy protocol to DataFrame memory models.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
Extend PyTorch Dataset/DataLoader API to build CXL memory pool-aware distributed data loading system — not a transparent solution, but explicitly leveraging CXL semantics (byte-level addressing, NUMA-aware, tiered placement) for global data pooling, intelligent prefetching, and load balancing across hundreds of TB of memory.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
The critical missing piece in the CXL-for-training stack is not hardware capability, but a fundamental rethinking of PyTorch's DataLoader architecture. This report provides source-level analysis of PyTorch DataLoader internals, identifies exact integration points for CXL, and systematically answers five critical design questions — from data path to architectural philosophy.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
GPU-Driven RDMA is not just 'GPU initiates RDMA' — it is the tight coupling of memory and communication in a multi-execution-unit system (GPU + NIC + CPU), analogous to the multi-core era where cache coherence and memory ordering became first-class design problems. This article explores the five fundamental challenges this convergence creates: data transfer, ordering, consistency, network parallelism, and memory flexibility.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
OpenURMA is an open-source FPGA implementation of the UB (Ultra-Bus) connectionless RDMA transport, achieving ≈500ns remote load/store latency on Alveo U50 (vs 2236ns on RoCE, 4.47× faster), while fully implementing the UB-Base-Specification 2.0.1 transaction and transport layers.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
Re-examining DeepSeek's MoE communication stack across five layers — from the physical network fabric up to global algorithmic scheduling — reveals that 'lock-free' and 'fully overlapped compute-communication' never actually eliminate synchronization. They redistribute it across physical-link queuing, microarchitectural bus arbitration, hardware-level memory-consistency fences, and algorithmic data-dependency waits.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
Ant Group's HSA-UltraLong demonstrates 16M token context via Hierarchical Sparse Attention. But sparsity dilutes during prefill — when sequence length grows 10×, a new storage tier with TB capacity and 500GB–1TB bandwidth becomes mandatory. This post analyzes the infra implications.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
DeepEP began as an EP acceleration library for MoE models. Its rebranding to "Every Parallelization" signals a structural ambition: becoming the thin data-transfer abstraction layer that unifies operators, QoS, and direct-drive capabilities across rack- and cluster-scale compute. This post traces that trajectory, draws the NCCL parallel, and argues why a new unified memory-data-movement service layer is emerging.
A showcase of AI-generated short videos — stunning fidelity, visceral impact, almost indistinguishable from reality. First installment of a multimodal AI survey series.
A showcase of AI-generated short videos — stunning fidelity, visceral impact, almost indistinguishable from reality. First installment of a multimodal AI survey series.
A showcase of AI-generated short videos — stunning fidelity, visceral impact, almost indistinguishable from reality. First installment of a multimodal AI survey series.
A showcase of AI-generated short videos — stunning fidelity, visceral impact, almost indistinguishable from reality. First installment of a multimodal AI survey series.
A showcase of AI-generated short videos — stunning fidelity, visceral impact, almost indistinguishable from reality. First installment of a multimodal AI survey series.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
DeepSeek-V4-Flash-0731 vs Kimi-K3 benchmark comparison. Kimi leads on all 5 shared benchmarks, but the two models target different segments — Flash efficiency vs flagship all-rounder.
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
以文言译英文原文，论多符预测（MTP）如何移推理之负载从访存密集转向计算密集，及其对芯片、互连、超节点之结构性冲击。
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepSeek's MTP doesn't just optimize inference — it restructures the compute-memory-interconnect triangle. This post analyzes the cascading impact on chip design, communication paradigms, supernode topology, and the competitive landscape of specialized silicon, with hard data from NVIDIA LPX, Groq Trillium, Cerebras WSE-3, and the linear-vs-sparse architecture war.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
DeepEP is not an All-to-All communication library — it is a Data Movement Runtime for MoE. Its core challenge is converting dynamic sparse Token-Expert mappings into continuous GPU-friendly data layouts. From first principles, this article analyzes DeepEP's complete design: layout transformation, Buffer pipeline, Warp Specialization, FIFO streaming, Metadata mapping, and the evolution toward Mega MoE.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
Warp Specialization transforms GPU from a batch-synchronous SIMT machine into a streaming dataflow engine. By assigning different Warps to different pipeline stages, it sacrifices local SIMD efficiency for global pipeline efficiency — the key to overlapping communication and compute in MoE dispatch, FlashAttention, and beyond.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
GPU's traditional execution model assumes Thread Blocks are independent — but modern AI workloads break this assumption. Hopper's two key evolutions — raising the Resident Block limit and introducing Thread Block Cluster — represent a fundamental shift: from optimizing single-Tensor-Core utilization to optimizing on-chip dataflow across the entire GPU.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is often discussed as a programming-ease feature. But the real reason we need an independent, global address service is deeper: it is about amortizing the cost of establishing communication pipes, enabling data services at every latency tier to exchange data with lower overhead. From Redis to NVLink, the history of AI distributed systems is a history of address services evolving toward lower latency.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Unified addressing is not a fixed implementation — it is a design philosophy. Location Transparency can be established at any scope and at any time. The current NVL72 global fabric is one deliberate trade-off among many. Future AI supernodes need a dynamic Address Resolution Service and an Object Directory.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
Claude Code runs tools locally — but products from DeepSeek, Manus, Kimi, Volcano Engine, Meituan, and E2B are building server-side execution into their core architecture. This post analyzes real product designs to explain why server-side CPU demand is growing.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
The frontier model landscape is splitting into two directions: cost-efficiency (DeepSeek) and high-precision (Kimi3, Claude, GPT). For AI infrastructure, this bifurcation is not a problem — it's a design constraint that defines the storage hierarchy.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
CXL is not just another interconnect protocol. From a market game theory lens, I believe CXL's long-term positioning is structurally underappreciated. Four forces are converging to make CXL the inevitable backbone of open-memory infrastructure.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
Google's KV Cache strategy isn't one solution — it's two complementary approaches: node-local tiered storage (HBM + CPU RAM + Local SSD) for single-node efficiency, and centralized Lustre for multi-tenant sharing. Both prove the same point: storage is the bottleneck.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
A faithful analysis of Kimi3's efficiency claims from the Linear paper, with cost modeling at million-token scale and the fundamental tradeoffs between linear and sparse attention architectures.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
Kimi3 represents the frontier of linear attention architectures — but can it scale? This post analyzes the micro-architecture war between linear attention (Kimi3, Qwen3) and sparse attention (DeepSeek V4, GLM52, LongCat), with quantitative analysis of their scaling ceilings.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
I ran an Agent session that accumulated 480 million tokens of context history. The bill revealed a truth that changes how we should think about AI infrastructure: at scale, storage costs dominate compute costs by orders of magnitude.
© 2026 backyes · Human-driven, AI-amplified · Built with Claude Code