📅 调度与批处理
调度器、批处理与 chunked prefill。
Taking vLLM Apart: A Practical Guide to Disaggregated Serving
『PD 分离服务』学习路径的核心实操文章:建议在理解 prefill/decode 差异并读过路由层介绍(如 vLLM Router 一文)之后阅读,再延伸到 Qwen3.8-2.4T PD 容量规划与 bidirectional KV transfer 等专题文章。
Announcing vllm-metal: Concurrent Serving on Apple Silicon
vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.
How we trained the fastest DSpark for Kimi-K3 using GB300 NVL72
本文是『推测解码』学习路径中的 DSpark 训练篇:应在掌握推测解码基础与 DFlash/DSpark 算法原理之后阅读,聚焦训练数据(hidden states)如何跨节点规模化生产。它排在两篇自适应验证文章之前,为理解 confidence head 驱动的硬件感知验证调度提供背景与动机。
vime × RL-Kernel × AMD: Bitwise Train–Rollout Consistency on ROCm
vime and RL-Kernel align selected-token logprobs bit for bit across Megatron training and vLLM rollout on AMD Instinct MI300X, with zero mismatches across 200 GRPO steps.
Kimi K3 Performance Optimizations in vLLM: The Road to 2.8× Throughput
Kimi K3 serving optimizations across scheduling, KDA prefix caching, ReplaySSM state recovery, PD disaggregation and state offload, parallelism, MoE, and GPU kernels.
Tiered KV Cache Offloading in vLLM
A host-centric framework for scaling KV cache across host memory, filesystems, object stores, and remote peers — reducing recomputation and increasing serving capacity.
Following the Bottleneck: Optimizing MiniMax M3 on AMD Instinct MI355X
A performance model for LLM serving: inspect local shapes, remove repeated work, verify data movement and dispatch, then follow the queue.
vLLM x AgentX: Optimizing for Real-World Agentic Serving
How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K tokens per GPU-second and a 14.6x-106x serving-cost advantage over Opus 5.
Serving LLMs on Tenstorrent Hardware: Inside the vLLM TT Plugin
Tenstorrent accelerators join vLLM as an out-of-tree platform plugin, driven by mesh-architecture choices: phase-based scheduling, single-process data parallelism on Galaxy, on-device sampling with host fallback, and async decode overlap.
IsoExec: Unified Execution to Eliminate Trainer-Inference Mismatch in SkyRL
IsoExec unifies numerical execution across SkyRL's vLLM and Megatron runtimes, reducing the average rollout-versus-training logprob difference below 1e-6 on Qwen3.5-35B-A3B with 25% overhead.
VeRL-Omni v0.2.0: Faster Diffusion RL and Stable Omni Training
A release focused on higher-throughput diffusion rollout, reusable omni adapters, and broader recipe coverage.
Distributed Layerwise Offload: Scaling Toward 200B+ DiT Models Efficiently in vLLM-Omni
Distributed Layerwise Offload shards and streams DiT weights across devices, serving a measured 124 GB Cosmos3 model on 64 GB HBM and estimating a path toward 200B+ models.
Adaptive Verification in vLLM: DSpark confidence-scheduled verification
本文是『推测解码』路径中自适应验证主题的第一篇，讲 DSpark 如何用置信度头按存活概率调度验证预算；下一篇是无 confidence head 的在线估计器方案，建议对比阅读以理解两类自适应验证的取舍。
Announcing Day-0 Support for NVIDIA Nemotron 3.5 Lightning on vLLM
How vLLM serves NVIDIA Nemotron 3.5 Lightning with OpenAI-compatible APIs, speculative decoding, and BF16/NVFP4 checkpoints across NVIDIA GPUs and edge systems.
vLLM Reaches 25K Total TPS/GPU on Qwen3.5
本文是『PD 分离服务』路径末尾三篇生产规模实战中建议最先读的一篇：它是 Qwen3.8 篇的直接前置（后者开篇引用本文结果），也是 GB200/GB300 NVL72 + 混合 GDN 架构 PD serving 的基础认知入口，可再顺读 GLM-5.2 篇对照 SLA 导向的取舍。
Optimizing vLLM on Arm CPUs
An overview of Arm CPU enablement and inference performance optimizations in vLLM.
Kimi K3 Is Here: Efficient Day-0 Support on vLLM
vLLM delivers day-0 Kimi K3 serving with hybrid KDA prefix caching, DSpark speculative decoding, production-scale disaggregation, and optimized kernels across NVIDIA and AMD GPUs.
Announcing vLLM AFD Plugin: Disaggregating Attention and FFN for Flexible MoE Serving
『PD 分离服务』路径的横向扩展篇:在掌握按阶段拆分的 PD 分离之后阅读,理解按组件(Attention vs FFN)拆分的另一维度;其异步 connector 本就部署在 PD 分离之上,与 hybrid SSM 篇(同一 PD 框架下的模型适配)互为补充。
From Day 0 to Production SLAs: Serving GLM-5.2 on 24 NVIDIA B300 GPUs with vLLM
本文是『PD 分离服务』学习路径末尾三篇生产规模实战之一，以 SLA 合规而非峰值吞吐为第一目标，与两篇 Qwen 吞吐导向文章形成方法论对照。建议在读完 Qwen3.5（25K TPS）篇建立 NVL72 + 混合架构 PD 基础后阅读，重点体会约束驱动的配置取舍与边界问题定位。
A Preview of Production-Scale Kimi K3 Support on vLLM
A preview of production-scale Kimi K3 support in vLLM, including KDA-aware prefix caching, fused kernels, optimized MXFP4 MoE, multimodal integration, and initial NVIDIA and AMD paths.
vLLM x TileRT: Specialized Decode for Latency-Critical Serving
『PD 分离服务』路径的生态应用篇:先理解 PD 分离动机与 vLLM V1 connector 接口,再读本文看 decode 侧可插拔的实际落地;三篇中最易读,适合作为路径收官时对“分离改变推理栈形态”的总结案例。
EAGLE3 Speculative Decoding on AMD Instinct GPUs: Training and Serving with vLLM and AMD Quark
『推测解码』学习路径中的动手实践深化篇:在掌握 speculative decoding 基本原理与 EAGLE 思路后阅读,可学到 EAGLE3 从训练、量化到服务落地的完整工程闭环;之后再读 2026-07-28 并行起草一文,了解超越 EAGLE-3 自回归限制的 P-EAGLE、DFlash、DSpark。
vLLM × HPC-Ops: High-Performance Attention and MoE Backends from Tencent Hunyuan
How HPC-Ops integrates Hopper-optimized attention and FP8 MoE backends into vLLM for Tencent Hunyuan Hy3, improving mixed-length decode, MoE latency, TTFT, and TPOT on NVIDIA H20.
Experience and Lessons Learned from Serving Multi-Stage Qwen3-Omni in vLLM-Omni
How vLLM-Omni serves and optimizes Qwen3-Omni with staged Thinker-Talker-Code2Wav execution, batching, CUDA Graphs, async chunk, async output, replicas, hot-path cleanup, and perf validation.
Engineering TTS Inference in vLLM-Omni
How vLLM-Omni supports and optimizes Qwen3-TTS, VoxCPM2, Higgs Audio V3, and Fish Speech S2 Pro with staged serving, batching, CUDA Graphs, and model-specific kernels.
MiniMax M3 in vLLM: Day-0 Serving for 1M-Token Multimodal Reasoning
How vLLM serves MiniMax M3 with MiniMax Sparse Attention, multimodal and reasoning parsers, MXFP8 weights, and long-context deployment recipes.
DiffusionGemma: The First Diffusion LLM (dLLM) Natively Supported in vLLM
How vLLM supports DiffusionGemma, the first native diffusion language model in vLLM, using Model Runner V2 state hooks, iterative denoising, bidirectional attention, and reused speculative decoding paths.
Fast & Efficient LLM Inference with vLLM: A New Course with DeepLearning.AI
What the DeepLearning.AI vLLM course teaches: optimizing, deploying, and benchmarking LLM inference with LLM Compressor quantization, GuideLLM, KV cache sizing, serving, and memory tradeoffs.
vLLM on the DGX Spark: Architecture, Configuration, and Local Evaluation
How to run vLLM on NVIDIA DGX Spark and GB10 systems, including unified memory behavior, NVFP4 Nemotron-3-Super serving, Docker deployment, Prometheus metrics, and local evaluation results.
Speculators v0.5.0: DFlash Support and Online Training
推测解码路径的中间篇:建议先读 Laguna XS.2 + DFlash 案例建立直观认识,再读本文掌握 DFlash 训练机制与 Speculators 训练生态,最后读 P-EAGLE 深入并行起草的架构与内核实现。
Native RL APIs in vLLM
How vLLM native RL APIs standardize weight syncing and asynchronous RL serving with NCCL and CUDA IPC transfer backends, pause mode, and fixes for fragile DPEP and disaggregated rollout deployments.
Accelerating Laguna XS.2 Inference with vLLM, Speculators, and LLM Compressor
推测解码学习路径的入门案例篇:建议最先阅读,用最短篇幅建立 DFlash 落地效果与生态协作的直观认识,再读 Speculators v0.5.0 了解训练细节,最后读 P-EAGLE 深入原理。
vLLM x Novita AI: PegaFlow for Production-Grade External KV Cache
How PegaFlow integrates with vLLM as an external KV cache service, using a Rust daemon, CUDA IPC, RDMA, SSD caching, and the external KV connector to improve startup, sharing, throughput, and cache lifecycle.
Announcing VeRL-Omni: Easy, Fast, and Stable RL Training for Diffusion and Omni-Modality Models
How VeRL-Omni extends verl with vLLM-Omni for reinforcement learning post-training of diffusion and multimodal generative models, including efficient rollouts, reward inference, trainers, hardware support, and recipes.
Elastic Expert Parallelism in vLLM
How Elastic Expert Parallelism lets vLLM scale Mixture-of-Experts serving up or down at runtime by changing data-parallel workers, redistributing experts, and coordinating live topology changes without server restarts.
A First Comprehensive Study of TurboQuant: Accuracy and Performance
A vLLM study comparing TurboQuant KV-cache quantization with BF16 and FP8 across long-context and reasoning workloads, showing where 4-bit variants help, where accuracy drops, and why FP8 remains the default choice.
Serving Agentic Workloads at Scale with vLLM x Mooncake
How vLLM integrates Mooncake Store as a distributed KV cache for agentic workloads, reusing shared prefixes across turns and instances to improve throughput, TTFT, end-to-end latency, and multi-GPU scaling.
DeepSeek V4 in vLLM: Efficient Long-context Attention
A first-principles walkthrough of DeepSeek V4's long-context attention, and how we implemented it in vLLM.
The State of FP8 KV-Cache and Attention Quantization in vLLM
What vLLM FP8 KV-cache validation found across Hopper and Blackwell, covering attention quantization, Flash Attention 3 fixes, memory savings, decode speedups, and layers to skip.
Disaggregated Serving for Hybrid SSM Models in vLLM
『PD 分离服务』路径的进阶深化篇:建议先读 vLLM 基础 PD 分离文章(介绍 NIXL connector 的 v0-disagg-prefill)掌握标准流程,再读本文理解 hybrid SSM 模型带来的底层改造;可与 AFD Plugin(按组件拆分)、TileRT(decode 可插拔)两篇生态文对照阅读。
vLLM Korea Meetup 2026 Wrap-Up
What the vLLM Korea Meetup 2026 covered: community growth, vLLM V1 updates, production stack adoption, accelerator integration, vllm-playground, and real-world LLM serving.
Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation
How single-node prefill/decode disaggregation in vLLM uses AMD MORI-IO on an 8-GPU MI300X node to separate prefill and decode, transfer KV cache efficiently, stabilize ITL, and improve goodput.
Extracting hidden states from vLLM
How vLLM extracts verifier hidden states through dummy draft models and KV Connector APIs for speculative decoding, enabling offline and online Speculators training without patching vLLM internals.
Model Runner V2: A Modular and Faster Core for vLLM
How Model Runner V2 reworks vLLM's execution core with modular model logic, GPU-native input preparation, stable persistent batching, async-first scheduling, and no API changes.
Beyond Porting: How vLLM Orchestrates High-Performance Inference on AMD ROCm
How vLLM orchestrates high-performance inference on AMD ROCm with multiple attention backends, workload-aware prefill, extend, and decode routing, AITER primitives, MLA support, and MI300X-class benchmarks.
Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock
How vLLM serves many fine-tuned MoE and dense models with Multi-LoRA, including fused MoE LoRA kernels, Triton compiler fixes, Split-K and CTA swizzling optimizations, and SageMaker AI and Bedrock tuning.
DeepSeek-V3.2 on GB300: Performance Breakthrough
What DeepSeek-V3.2 and DeepSeek-R1 benchmark results show on NVIDIA GB300 with vLLM, covering NVFP4 quantization, TP and EP deployment, throughput, and reproducible setup details.
Driving vLLM WideEP and Large-Scale Serving Toward Maturity on Blackwell (Part I)
How vLLM improves WideEP and large-scale DeepSeek-style MoE serving on NVIDIA GB200 with NVFP4 and FP8 kernels, fusion, prefill/decode disaggregation, weight offloading, and reduced chunking overhead.
GPT-OSS Performance Optimizations on NVIDIA Blackwell: Pushing the Pareto Frontier
How vLLM and NVIDIA optimized GPT-OSS on Blackwell with FlashInfer, torch.compile fusion, FP8 KV cache, async scheduling, stream interval tuning, and deployment recipes that improve throughput and interactivity.
Streaming Requests & Realtime API in vLLM
How vLLM supports streamable inputs and a Realtime WebSocket API for audio, video, robotics, and low-latency applications that need incremental input processing instead of complete prompts.
vLLM Large Scale Serving: DeepSeek @ 2.2k tok/s/H200 with Wide-EP
How vLLM reaches 2.2k tokens per second per H200 for DeepSeek-style MoE serving with Wide-EP, async scheduling, dual-batch overlap, disaggregated serving, CUDA graphs, DeepGEMM, and expert load balancing.
AMD × vLLM Semantic Router: Building the System Intelligence Together
How AMD and vLLM Semantic Router build GPU-accelerated Mixture-of-Models routing with signals, semantic caching, response storage, PII, jailbreak, and hallucination guardrails.
Encoder Disaggregation for Scalable Multimodal Model Serving
How vLLM EPD separates visual encoders from text prefill and decode, covering LMM serving, GPU resource scaling, multimodal interference, and pipelined execution.
Diving into speculative decoding training support for vLLM with Speculators v0.3.0
『推测解码』路径的第二篇（工程进阶）：承接第一篇对 EAGLE/Medusa 的概念铺垫，深入 Eagle3 draft model 的训练数据构造、训练技术与 vLLM 部署细节，建议在读过入门篇后、读 EAGLE 3.1 之前阅读。
Building Clean, Maintainable vLLM Modifications Using the Plugin System
How the vLLM plugin system helps teams customize scheduling, KV-cache behavior, hardware integrations, and model execution without long-lived forks, monkey patches, or brittle internal modifications.
Fast and Affordable LLMs serving on Intel Arc Pro B-Series GPUs with vLLM
How vLLM serves LLMs on Intel Arc Pro B-Series GPUs with MoE optimizations, persistent kernels, multi-GPU scaling, LoRA, speculative decoding, structured outputs, and mixed-precision recipes.
vLLM TPU: A New Unified Backend Supporting PyTorch and JAX on TPU
How the redesigned vLLM TPU backend uses tpu-inference, JAX-to-XLA lowering, Torchax, ragged paged attention, and unified PyTorch and JAX support to improve TPU performance and model coverage.
SemiAnalysis InferenceMAX: vLLM and NVIDIA Accelerate Blackwell Inference
How vLLM and NVIDIA optimize Blackwell inference for SemiAnalysis InferenceMAX, improving gpt-oss 120B and Llama 3.3 70B throughput with FP4 kernels, scheduling work, and Pareto-frontier benchmarking.
DeepSeek-V3.2-Exp in vLLM: Fine-Grained Sparse Attention in Action
How vLLM supports DeepSeek-V3.2-Exp with DeepSeek Sparse Attention, lightning indexer caches, separate prefill and decode paths, FlashMLA sparse attention, DeepGEMM kernels, and Blackwell deployment.
The First vLLM Meetup in Korea
What the first vLLM Korea meetup covered: community adoption, llm-d, TPU integration, contribution workflows, hardware plugin architecture, Rebellions NPU work, and production inference lessons.
Inside vLLM: Anatomy of a High-Throughput LLM Inference System
How vLLM's inference engine works, covering PagedAttention, continuous batching, prefix caching, speculative decoding, multi-GPU serving, scheduling, and benchmarking for high-throughput LLM workloads.
Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU
How vLLM hardware plugins decouple backend integrations from core vLLM, using Platform, Executor, Worker, ModelRunner, AttentionBackend, and Communicator hooks to support Ascend NPU and IBM Spyre.
Transformers modeling backend integration in vLLM
How vLLM integrates the Hugging Face Transformers modeling backend to serve more model architectures efficiently, including text and vision-language models through model_impl=\"transformers\".
PTPC-FP8: Boosting vLLM Performance on AMD ROCm
How PTPC-FP8 quantization improves vLLM performance on AMD ROCm by combining per-token activation scaling and per-channel weight scaling for near-BF16 accuracy with FP8 speed.
Distributed Inference with vLLM
A guide to distributed inference in vLLM, covering tensor parallelism, pipeline parallelism, multi-GPU and multi-node serving, KV cache challenges, speculative decoding, communication kernels, and control-plane design.
vLLM V1: A Major Upgrade to vLLM's Core Architecture
What changed in vLLM V1: a re-architected engine with a simpler scheduler, near-zero-overhead prefix caching, cleaner tensor parallelism, multiprocessing API server, and default optimizations for higher-throughput serving.
High Performance and Easy Deployment of vLLM in K8S with vLLM production-stack
What vLLM production-stack adds for Kubernetes serving: prefix-aware routing, LMCache-backed KV cache sharing, autoscaling, observability, fault tolerance, and cluster deployment with higher throughput and lower latency.
Structured Decoding in vLLM: a gentle introduction
How structured decoding works in vLLM, covering JSON outputs, grammar-guided generation, outlines, XGrammar, TPOT improvements, constrained decoding, and agentic workflow use cases.
vLLM 2024 Retrospective and 2025 Vision
A vLLM 2024 retrospective and 2025 roadmap covering community growth, model and hardware support, production adoption, office hours, ecosystem partnerships, and the path toward universal open-source serving.
Serving LLMs on AMD MI300X: Best Practices
Best practices for serving LLMs with vLLM on AMD MI300X, covering ROCm setup, Llama 3.1 70B and 405B benchmarks, chunked prefill, multi-step scheduling, prefix caching, graph capture, and AMD tuning.
How Speculative Decoding Boosts vLLM Performance by up to 2.8x
『推测解码』学习路径的第一篇（入门）：先读它建立 draft/target 协作、n-gram、Medusa/EAGLE 等基础概念和 vLLM 系统架构认知，再进入后两篇分别学习 Eagle3 draft model 的训练工程与 EAGLE 3.1 的算法改进。
vLLM v0.6.0: 2.7x Throughput Improvement and 5x Latency Reduction
What changed in vLLM v0.6.0 to improve throughput and latency, including API server isolation, reduced CPU overhead, multi-step scheduling, async execution, and benchmarks against earlier vLLM versions.
vLLM’s Open Governance and Performance Roadmap
vLLM's open governance and performance roadmap, covering LF AI and Data incubation, public benchmarks, optimized kernels, async scheduling, API frontend overhead, torch.compile, disaggregated prefill, and community research.
Announcing Llama 3.1 Support in vLLM
How vLLM supports Meta Llama 3.1 models, including 128K context, Llama 3.1 405B serving, chunked prefill, FP8 quantization, tensor and pipeline parallelism, CPU offloading, and early performance results.
Notes on vLLM v.s. DeepSpeed-FastGen
A performance comparison of vLLM and DeepSpeed-FastGen, explaining when Dynamic SplitFuse helps, where vLLM is faster, and how memory allocation, output length, and workload shape affect throughput.