Vllm
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About Vllm
vLLM is an open-source high-throughput and memory-efficient LLM inference and serving engine, created by Woosuk Kwon, Zhuohan Li, and colleagues at UC Berkeley in 2023. vLLM's breakthrough innovation is PagedAttention — a memory management technique inspired by virtual memory paging in OS kernels that eliminates memory fragmentation in the KV cache, increasing GPU memory utilization from ~60% (with naive continuous batching) to 96%+. PagedAttention enables vLLM to serve 2-24x more requests per second than HuggingFace Transformers Inference at the same batch size. vLLM supports continuous batching (new requests join mid-batch), tensor parallelism across multiple GPUs, pipeline parallelism across nodes, and CUDA graph capture for low-latency single requests. vLLM's OpenAI-compatible API server (`python -m vllm.entrypoints.openai.api_server`) makes it a drop-in high-performance replacement for OpenAI's endpoint. vLLM supports Llama, Mistral, Gemma, Qwen, DeepSeek, Falcon, Phi, BLOOM, Yi, and 30+ model architectures. Quantization support includes GPTQ, AWQ, SqueezeLLM, and FP8 for reducing GPU memory requirements. vLLM's speculative decoding accelerates inference by using a small draft model to propose tokens verified by the main model in parallel. vLLM reached 20,000+ GitHub stars within months of release and powers LLM inference at Anyscale, Google Cloud, IBM, and most enterprise AI platforms. Anyscale's LLM endpoints and AWS Bedrock use vLLM internally. vLLM's production adoption rivals commercial serving solutions from NVIDIA (TensorRT-LLM) and Meta (TGI).
Frequently Asked Questions
What is PagedAttention in vLLM?
PagedAttention is vLLM's core optimization that manages GPU KV cache memory using fixed-size pages (like OS virtual memory), instead of allocating contiguous memory per sequence. This eliminates internal and external fragmentation, allowing near-100% GPU memory utilization and enabling much larger effective batch sizes. The result: 2-24x higher throughput than naive inference for the same GPU hardware.
vLLM vs Ollama — which should I use?
Ollama for local development, single-user inference, and privacy-first scenarios where a MacBook or developer workstation is sufficient. vLLM for production serving with multiple concurrent users, GPU cluster deployments, or when throughput (requests/second) matters. vLLM requires CUDA GPUs and is designed for server-side deployment, not consumer hardware.
Can vLLM run on AMD GPUs or non-NVIDIA hardware?
vLLM's primary support is NVIDIA CUDA. AMD ROCm support exists experimentally (ROCM builds available). Apple Metal (M-series) is not supported — use Ollama or llama.cpp for Apple Silicon. Google TPU and Intel Gaudi support has been added in later vLLM releases as enterprise hardware interest grew.
Top Alternatives to Vllm
Ollama
Local single-user inference — Ollama for developer laptops, vLLM for GPU cluster production serving
TGI
Hugging Face Text Generation Inference — comparable production server, different optimization stack
TensorRT-LLM
NVIDIA's LLM inference optimizer — maximum NVIDIA GPU throughput with more complex setup than vLLM
Ray Serve
Scalable model serving framework — vLLM often runs inside Ray Serve for distributed GPU fleets
SageMaker
AWS managed ML serving — SageMaker can serve vLLM containers for managed inference endpoints
Triton
NVIDIA Triton Inference Server — enterprise model serving supporting TensorRT-LLM and vLLM backends
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