📐 量化
FP8 / NVFP4 / MXFP4 / INT4 权重与 KV cache 量化的精度与性能权衡。
Finish RL Training Faster: Bitwise-Consistent, High-Performance, Agent-Driven
We make vLLM rollout and the training forward give bitwise identical logprobs on DeepSeek-V4-Flash and Nemotron-3.5-Lightning-30B-A3B (NVFP4), keep 91% of rollout throughput on DeepSeek-V4-Flash, and open-source the agent skill.
Improve TTFT for Multi-Turn Agentic Workloads with Bi-Directional KV Transfer in vLLM
本文是『PD 分离服务』路径中面向多轮/agentic 场景的进阶篇，应在掌握 P-D 分离与默认 NIXL pull connector 后阅读。它与 push connector 一文（2026-10-14）互补：push 优化单请求首响传输路径，本文解决跨轮 KV 复用，且双向能力目前构建在 pull 模式的 NixlConnector 之上。
vLLM Support for NVIDIA Vera Rubin NVL72: 7.8x Throughput over GB200 NVL72
vLLM now runs on NVIDIA Vera Rubin NVL72 with day-0 model support, Rubin-tuned FlashInfer kernels, and locality-aware MoE, delivering 7.8x the per-GPU throughput of GB200 NVL72 on AgentX.
DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0
Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.
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。
PD Serving of Qwen3.8-2.4T
本文是『PD 分离服务』路径末尾三篇生产实战的收官篇，开篇即引用 Qwen3.5（25K TPS）篇的结果并把目标从“冲吞吐”扩展为完整 Pareto 前沿。建议三篇中最后阅读，重点吸收其分步测量、先估容量再选拓扑的可复用调优方法论。
vLLM x Novita AI: Chord, Faster INT4 MoE for Kimi K2.x. Up to 1.3x on H200, 2.15x on Untuned B300
Novita AI has open-sourced Chord, a high-performance W4A16 MoE CUDA kernel for Kimi K2.x serving shapes, with a Humming-compatible indexed path and grouped SM90 operators.
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。
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.
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.
GLM 5.3 Optimizations, Part 1: Hybrid HiSparse Offloading in vLLM
vLLM integrates HiSparse as a pressure-driven memory tier that composes with the Hybrid Memory Allocator and KV offloading, letting GLM 5.3 requests keep decoding when their KV no longer fits in GPU memory, so concurrency stays high.
MiniMax H3 on vLLM-Omni: From System-Wide Optimization to Real-Time Serving with FastVideo’s FastH3
How vLLM-Omni optimizes and scales the complete MiniMax H3 stack, then integrates FastVideo’s four-step FastH3 for generation faster than playback.
Large-Scale Sharded Weight Transfer with Ray Direct Transport (RDT) in vLLM
We implement a native sharded weight transfer engine in vLLM utilizing Ray Direct Transport (RDT), achieving weight transfer for the Kimi K2 model in BF16 on 48 8xH100 nodes in 7.53s
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。
Day 0 Support for Qwen3.8-2.4T-A95B on vLLM
Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed kernels on NVIDIA and AMD hardware.
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.
Efficient Decode Context Parallelism with vLLM for Long Context Workloads
Decode Context Parallelism (DCP) in vLLM shards KV cache across GPUs by sequence dimension, enabling 3× higher throughput on long-context agentic workloads compared to standard tensor parallelism.
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.
TML Inkling on vLLM: Day-0 Support with Optimized Performance
vLLM brings day-0 support to TML Inkling, a 1T-parameter multimodal model, with MTP, long-context serving, parallelism, and up to 380 tokens per second per user on NVIDIA GB200 GPUs.
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。
vime + ROCm: End-to-End RL Post-Training on AMD Instinct™ GPUs
Announcing ROCm support for vime, now running end-to-end on AMD Instinct MI355X GPUs with prebuilt container.
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.
Announcing vime: A Simple, Stable, and Efficient RL Framework for LLMs
vime connects slime's training stack with vLLM rollouts to provide a simple, stable, and efficient RL post-training pipeline.
Announcing Day-0 Support for NVIDIA Nemotron 3 Ultra on vLLM
How to serve NVIDIA Nemotron 3 Ultra with vLLM for long-running agentic reasoning, including BF16 and NVFP4 checkpoints, supported GPU configurations, OpenAI-compatible deployment, and NeMo RL integration.
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.
Accelerating vLLM-Omni Inference with AutoRound Quantization
How AutoRound integrates with vLLM-Omni to serve W4A16 quantized multimodal, diffusion, image, and video models with smaller checkpoints, preserved quality, Intel XPU acceleration, and NVIDIA GPU support.
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。
EAGLE 3.1: Advancing Speculative Decoding Through Collaboration Between the EAGLE Team, vLLM, and TorchSpec
『推测解码』路径的第三篇（进阶收尾，也是当前最新进展）：默认读者已掌握前两篇的推测解码原理与 Eagle3 hidden-state 机制，聚焦 EAGLE 3.1 对 attention drift 的架构级修复与生产落地，建议最后阅读。
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.
vLLM Tops the Artificial Analysis Leaderboard
How vLLM achieved leading Artificial Analysis results for DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B using open-source kernel fusion, speculative decoding, Blackwell optimizations, and model-specific serving work.
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.
Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM
How to serve NVIDIA Nemotron 3 Nano Omni with vLLM for multimodal agentic AI, including BF16, FP8, and NVFP4 checkpoints, vision/audio/video inputs, supported GPUs, OpenAI-compatible APIs, and deployment recipes.
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。
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.
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.
P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM
推测解码学习路径中的深度原理压轴篇:建议先读 Laguna XS.2 + DFlash 案例建立直观认识,再读 Speculators v0.5.0 掌握训练生态,最后读本文深入并行起草的架构、训练算法与 vLLM 内核级实现。
Run Highly Efficient and Accurate Multi-Agent AI with NVIDIA Nemotron 3 Super Using vLLM
How to serve NVIDIA Nemotron 3 Super with vLLM for multi-agent AI, including BF16, FP8, and NVFP4 checkpoints, 1M-token context, Thinking Budget, MTP, supported GPUs, and OpenAI-compatible deployment.
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.
vLLM-Omni Diffusion Cache Acceleration
How vLLM-Omni speeds up diffusion model inference with Cache-DiT and TeaCache, reusing intermediate computations across timesteps to deliver 1.5x to 2x image generation speedups with minimal quality loss.
Run Highly Efficient and Accurate AI Agents with NVIDIA Nemotron 3 Nano on vLLM
How to serve NVIDIA Nemotron 3 Nano with vLLM for efficient agentic AI, including BF16, FP8, and NVFP4 checkpoints, 1M-token context, hybrid MoE architecture, Thinking Budget, supported GPUs, and OpenAI-compatible deployment.
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。
Advancing Low‑Bit Quantization for LLMs: AutoRound x LLM Compressor
How Intel AutoRound integrates with LLM Compressor to produce low-bit quantized checkpoints for vLLM, using tuning-based PTQ, W4A16 and related formats, compressed-tensors compatibility, and lightweight calibration.
Announcing vLLM-Omni: Easy, Fast, and Cheap Omni-Modality Model Serving
What vLLM-Omni adds to the vLLM ecosystem: omni-modality serving for text, image, video, and audio, diffusion and non-autoregressive generation support, disaggregated stages, OpenAI-compatible APIs, and pipelined execution.
Docker Model Runner Integrates vLLM for High-Throughput Inferencing
How Docker Model Runner integrates vLLM as an inference backend, letting developers run safetensors models with high-throughput serving, PagedAttention, streaming, and OpenAI-compatible APIs from Docker workflows.
Shared Memory IPC Caching: Accelerating Data Transfer in LLM Inference Systems
How shared memory IPC caching in vLLM reduces redundant data transfers for multimodal and multi-process inference, improving prefill throughput and TTFT by sharing large inputs across coordinator and worker processes.
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.
Run Multimodal Reasoning Agents with NVIDIA Nemotron on vLLM
How to serve NVIDIA Nemotron Nano 2 VL with vLLM for multimodal reasoning agents, including video understanding, document intelligence, Efficient Video Sampling, 128K context, and OpenAI-compatible deployment.
From Monolithic to Modular: Scaling Semantic Routing with Extensible LoRA
How vLLM Semantic Router refactors its Rust classification layer with modular model support, Qwen3-Embedding, EmbeddingGemma, LoRA-based multi-task classification, and concurrent routing execution.
Zero-Reload Model Switching with vLLM Sleep Mode
How vLLM Sleep Mode enables fast model switching by hibernating weights to CPU RAM or discarding them while preserving process state, CUDA graphs, allocators, and kernel warmup to avoid full reloads.
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.
Introduction to torch.compile and How It Works with vLLM
How torch.compile works inside vLLM, including TorchDynamo graph capture, TorchInductor code generation, custom compiler passes, graph breaks, model compilation strategy, and performance optimization.
GLM-4.5 Meets vLLM: Built for Intelligent Agents
How to run GLM-4.5 and GLM-4.5V with vLLM for intelligent agents, including hybrid reasoning modes, FP8 and BF16 serving, multimodal support, and NVIDIA Blackwell and Hopper deployment.
vLLM Now Supports gpt-oss
How vLLM supports gpt-oss 20B and 120B on NVIDIA Blackwell, Hopper, and AMD GPUs, with MXFP4 MoE kernels, efficient attention, hybrid KV cache allocation, and built-in tool support.
MiniMax-M1 Hybrid Architecture Meets vLLM: Long Context, Fast Inference
How vLLM serves MiniMax-M1's hybrid MoE architecture for long-context inference, covering model deployment, memory management, batched serving, backend optimizations, and Docker-based setup.
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.
Llama 4 in vLLM
How vLLM serves Meta Llama 4 Scout and Maverick multimodal MoE models with long-context support, tensor parallel deployment, H100 and H200 guidance, FP8 variants, and performance tips.
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.
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.
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.