vLLM
High-throughput and memory-efficient LLM serving engine powered by PagedAttention.
About vLLM
vLLM is the industry-standard open-source LLM serving and inference engine designed for ultra-high throughput and minimal memory waste. Developed by UC Berkeley researchers, vLLM introduced PagedAttention—a revolutionary memory management algorithm that manages attention key-value (KV) cache like virtual memory in operating systems, virtually eliminating memory fragmentation. Capable of delivering 2x to 4x higher throughput than Hugging Face TGI and standard PyTorch runtimes, vLLM powers production AI inference infrastructure across enterprise cloud clusters and high-volume API providers worldwide.
vLLM features state-of-the-art inference optimizations including continuous request batching, Chunked Prefill, speculative decoding, prefix caching, and native quantization support (AWQ, GPTQ, FP8, INT4, SqueezeLLM). It provides drop-in OpenAI-compatible REST API endpoints, supports multi-GPU distributed tensor parallelism with Ray/NCCL, and serves all major model architectures including DeepSeek-V3, Llama 3.3, Mistral, Qwen 2.5, and Command R+.
Step 1: Install vLLM with `pip install vllm` on an Nvidia CUDA or AMD ROCm system.
Step 2: Start an OpenAI-compatible server using `vllm serve meta-llama/Llama-3.3-70B-Instruct`.
Step 3: Configure tensor parallelism across GPUs using `--tensor-parallel-size 4`.
Step 4: Send HTTP requests to `http://localhost:8000/v1/chat/completions` with high concurrency.
Capabilities & Features
Common Use Cases
High-concurrency LLM API serving with continuous request batching
Cost-efficient self-hosted inference for DeepSeek, Llama 3, and Mistral models
Low-latency speculative decoding and prefix-cached conversational chatbots
Quantized FP8 and AWQ deployment on Nvidia GPUs
Frequently Asked Questions
What makes vLLM so fast?
vLLM uses PagedAttention to allocate KV cache in non-contiguous memory blocks, eliminating memory fragmentation and allowing near-100% GPU memory utilization for batching.
Can vLLM replace the OpenAI API?
Yes. vLLM exposes an identical /v1/chat/completions endpoint, allowing you to swap proprietary APIs with self-hosted open-weights models seamlessly.
Does vLLM support DeepSeek-V3 and Llama 3 models?
Yes. vLLM provides day-zero support for DeepSeek-V3, Llama 3.3, Qwen 2.5, and all modern MoE architectures.
Free Plan
100% Free, open-source inference engine under Apache 2.0 license
Paid Plan
No software fee; deploy on your own GPU instances (RunPod, AWS, Lambda, GCP)
Direct link · Verified & reader-supported
Pros & Cons
PagedAttention delivers up to 4x higher throughput with near-zero KV cache fragmentation
Drop-in OpenAI-compatible API server enables instant client integration
Extensive quantization support (FP8, AWQ, GPTQ) for running huge models on fewer GPUs
Continuous batching and chunked prefill minimize TTFT and maximize concurrency
Optimized primarily for Linux GPU environments (Nvidia CUDA / AMD ROCm)
Requires GPU memory planning and tensor parallelism configuration for multi-GPU nodes
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