"Bỏ qua để đến phần nội dung chính\nHỗ trợ truy cập\nĐăng nhập\nChế độ AI\nTất cả\nVideo ngắn\nVideo\nHình ảnh\nMua sắm\nTin tức\nThêm\nCông cụ\nKết quả tìm kiếm\nLooking for results in English?\nChange to English\nTiếp tục sử dụng Tiếng Việt\nCài đặt ngôn ngữ\nThông tin tổng quan do AI tạo\nPrefill-Decode Disaggregation splits LLM inference into two dedicated steps to prevent resource contention on a single node. Because prefilling large prompts requires heavy computation while decoding tokens requires high memory bandwidth, separating them lets both run smoothly without delaying each other, boosting throughput by up to 2.5× on single nodes. \nvLLM\nThe Mechanics: Why You Need It\nA standard vLLM setup forces both the prefill and decode stages to share the same GPUs. This causes a few bottlenecks: \nvLLM\nPrefill is compute-bound. It processes the entire user prompt all at once using heavy matrix math, demanding maximum processing power.\nDecode is memory-bandwidth-bound. It spits out tokens one by one and requires rapid, repetitive reading of weights from memory. \nvLLM\n +4\nWhen these two phases share the same GPU pool, they fight for scheduling. A large prefill can severely block ongoing decode requests, causing visible stuttering and unpredictable spikes in Inter-Token Latency (ITL) (the time it takes for your model to generate consecutive words). \nvLLM\nSingle-Node Disaggregation\nYou do not need a massive multi-node cluster to fix this. By utilizing advanced connections (such as AMD's MORI-IO or the NIXL-based KV connectors) directly on single servers like an 8-GPU MI300X node, you can split your hardware: \nvLLM\n +1\nDedicated Prefill Instances: Purely process prompt inputs and compute the KV Cache (the model's memory of the conversation so far).\nDedicated Decode Instances: Continuously generate text for active users without interruption.\nKV Cache Transfer: The computed KV cache data is passed from the prefill GPUs to the decode GPUs via RDMA (Remote Direct Memory Access, which lets computers transfer data rapidly over a network or internal fabric). \nvLLM\n +3\nThe Trade-Offs\nWhile disaggregation creates very stable and predictable generation speeds, it does carry trade-offs: \nvLLM\nTime-to-First-Token (TTFT): The time a user waits to see the first word of a response can slightly increase. The decode instance may have to wait in a \"remote KVS\" state while the prefill data transfers over. \nvLLM\nWrite vs. Read Mode: \"Write mode\" overlaps prefill computation with data transfer, giving better TTFT. \"Read mode\" is simpler to set up but leads to a longer wait for the first token. \nvLLM\nIf your workload experiences a mix of short and long prompts, or if your application frequently hits heavy concurrent traffic, separating these workloads stabilizes your system and delivers a significantly better user experience. \nJarvis Labs\n +1\nIf you tell me more about your current workload, such as average prompt lengths, traffic concurrency, and hardware (e.g., NVIDIA vs. AMD), I can help you decide whether full disaggregation or chunked prefill is the better fit for your setup.\n6 trang web\nWhy Your Single-Node vLLM Setup Needs Prefill-Decode ...\n7 thg 4, 2026 — * TL;DR: Prefill and decode fight over the same GPUs, causing ITL spikes under load. ... * Prefill is compute-bound: it processes ...\nvLLM\nDisaggregated Serving for Hybrid SSM Models in vLLM\n21 thg 4, 2026 — vLLM already supports disaggregated prefill/decode (P/D) for standard transformer models through its NIXL-based KV connector: a pr...\nvLLM\nPrefill Is Compute-Bound. Decode Is Memory-Bound. Why... - daily.dev\n15 thg 4, 2026 — Decode Is Memory-Bound. Why Your GPU Shouldn't Do Both. LLM inference has two fundamentally different phases: prefill (compute-bou...\ndaily.dev\nHiện tất cả\nHiện thêm\nWeb results\nWhy Your Single-Node vLLM Setup Needs Prefill-Decode ...\n\nvLLM\nhttps://vllm.ai › blog › 2026-04-07-m...\n·\nDịch trang này\n7 thg 4, 2026 — Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation · Figure 1: Read mode request flow. · Figure 2: Write ...\ndisaggregation - Blog\n\nvLLM\nhttps://vllm.ai › blog › tags › disaggre...\n·\nDịch trang này\nNext-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation ... How single-node prefill/decode disaggregation in vLLM uses AMD MORI- ...\nNext-Level Inference: Why Your Single-Node vLLM Setup...\n\nDaily.dev\nhttps://daily.dev › vLLM\nNext-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation ... Prefill and decode phases in LLM inference compete for the same GPU ...\nvLLM Blog | vLLM is a fast and easy-to-use library for LLM ...\n\nGitHub\nhttps://vllm-project.github.io\n·\nDịch trang này\n... Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation. Apr 2, 2026. Announcing Gemma 4 on vLLM: Byte for byte, the most ...\n단일 노드 환경에서도 vLLM 처리량을 2.5배나 끌어올린 ...\n\nThreads\nhttps://www.threads.com › post › 단...\n·\nDịch trang này\n18 thg 4, 2026 — ... Node vLLM Setup Needs Prefill-Decode. vllm.ai. Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation. 2.\nDisaggregated Inference at Scale with PyTorch & vLLM –...\n\ndaily.dev\nhttps://daily.dev › PyTorch\n12 thg 9, 2025 — ... Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation. View all. Best discussions. Windows finally fixed ...\nPrefill-Decode Disaggregation in Practice: Governing TTFT and ...\n\noh-bug.com\nhttps://oh-bug.com › Articles\n·\nDịch trang này\nvLLM Blog: Next-Level Inference — Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation · Prefill-Decode Aggregation or Disaggregation? Unifying ...\nBlog\n\nBlog | vLLM\nhttps://vllm.ai › blog\n·\nDịch trang này\n... Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation. Apr 7, 2026 ·22 min read. How single-node prefill/decode ...\nTorchDigest\n\ntanaysood.ai\nhttps://torchdigest.tanaysood.ai\n·\nDịch trang này\nRelatedvLLMNext-Level Infe"