"Bỏ qua để đến phần nội dung chính\nHỗ trợ truy cập\nĐăng nhập\nChế độ AI\nTất cả\nHình ảnh\nVideo\nTin tức\nMua sắm\nVideo ngắn\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\nProcessing 1M-token contexts in SGlang requires splitting the model into separate Prefill (compute) and Decode (memory) instances. This PD Disaggregation tackles 1M-token sequences by pushing the memory-intensive KV cache (information storage) to decode instances while offloading the heavy math to prefill. However, transferring this massive amount of data creates extreme memory and bandwidth hurdles. \narXiv\n +2\nUnderstanding the 1M-Token Memory Challenge\nIn LLMs, the KV cache is the memory used to store the \"attention\" of every token generated so far. For a 1 million token sequence, the KV cache footprint alone scales massively, causing severe issues: \nGitHub\n +2\nOOM (Out-of-Memory) Handoffs: The prefill node must transfer the complete, gigabyte-sized KV cache of the 1M tokens to the decode nodes. This frequently crashes memory pools on the decode side. \nGitHub\n +1\nAdmission Bottlenecks: When a decode instance receives a massive pre-allocated chunk from a 1M-token prompt, it risks immediately freezing. In production, this can lock up all other requests. \nGitHub\nQuadratic Math Complexity: The processing time for 1M tokens grows exponentially (O(N²)), causing immense latency. \nGitHub\nHiện thêm\nWeb results\n[Bug] DeepSeek-V4-Pro，PD Disaggregation Input Length ...\n\nGitHub\nhttps://github.com › sglang › issues\n·\nDịch trang này\n6 thg 5, 2026 — The current implementation can already support around 1M context in our setup, but we are still validating accuracy/stability, especially for ...\nPipeline Parallelism in SGLang: Scaling to Million-Token ...\n\nLMSYS Org\nhttps://www.lmsys.org › blog › 2026-...\n·\nDịch trang này\n15 thg 1, 2026 — A unique strength of SGLang is its native support for Prefill-Decode (PD) Disaggregation within a pipelined setup. In a disaggregated cluster, ...\nPD Disaggregation - SGLang Documentation\n\nSGLang Documentation\nhttps://docs.sglang.ai › pd_disaggrega...\n·\nDịch trang này\nTo address these challenges, we introduce Prefill and Decoding (PD) Disaggregation in SGLang. . Issues with Unified Scheduling. The conventional unified engine ...\nBị thiếu: 1M challenge\n1 Introduction\n\narXiv\nhttps://arxiv.org › html\n·\nDịch trang này\n27 thg 1, 2026 — In this paper, we propose LAPS, a prefill-length-aware LLM serving system built on the PD disaggregation paradigm to optimize heterogeneous ...\nBị thiếu: challenge | Phải có: challenge\n[Bug] PD disaggregated ,The token in the log is ...\n\nGitHub\nhttps://github.com › sglang › issues\n·\nDịch trang này\n24 thg 6, 2025 — I use 4P+5D and I encountered the same problem. After using a long text data set with a length of 20,000 to 30,000 tokens for continuous stress ...\nBị thiếu: 1M challenge\nPipeline Parallelism for Long Context - SGLang Documentation\n\nSGLang Documentation\nhttps://lmsysorg.mintlify.app › docs\n·\nDịch trang này\nSGLang has supported Pipeline Parallelism (#5724) for some time and made it compatible with the PD Disaggregation feature (#8846), but the implementation was ...\nBị thiếu: 1M challenge\nServer Arguments - SGLang Documentation\n\nSGLang Documentation\nhttps://sgl-project.github.io › server_a...\n·\nDịch trang này\nThis page provides a list of server arguments used in the command line to configure the behavior and performance of the language model server during ...\nBị thiếu: 1M challenge\nPrefill-Decode Disaggregation in SGLang: Why Splitting LLM ...\n\nkuncoro.io\nhttps://kuncoro.io › blog › prefill-deco...\n·\nDịch trang này\n1 thg 2, 2026 — How SGLang's PD disaggregation separates the compute-heavy prefill phase from memory-bound decode, delivering 2-3x throughput gains for ...\nBị thiếu: 1M challenge\n[Feature]Prefill Context Parallel with Zigzag Ring Attention ...\n\nGitHub\nhttps://github.com › sglang › issues\n·\nDịch trang này\n6 thg 4, 2026 — In a PD-disaggregated deployment, the prefill node must also transfer the full KV cache to the decode node — amplifying both memory and ...\nBị thiếu: 1M challenge\nĐiều hướng trang\n\t\n1\t\n2\n\t\n3\n\t\n4\n\t\n5\n\t\n6\n\t\n7\n\t\n8\n\t\n9\n\t\nTiếp\nĐường liên kết ở chân trang\nViệt Nam\nTừ Liêm, Hà Nội - Dựa trên hoạt động trước đây của bạn\n - Cập nhật vị trí\nTrợ giúp\nGửi phản hồi\nQuyền riêng tư\nĐiều khoản"