vLLM

vLLM is the highest-throughput open-source serving engine for large language models. Built around PagedAttention — a memory management technique inspired by operating system virtual memory — it achieves 2-4x higher throughput than naive implementations by eliminating memory waste in the KV cache. When you need to serve dozens or hundreds of concurrent users from a single GPU cluster, vLLM is the production-grade solution.

Key Features

PagedAttention memory management. Traditional LLM serving pre-allocates contiguous memory blocks for each request’s KV cache, wasting 60-80% of GPU memory on internal fragmentation. PagedAttention stores KV cache in non-contiguous pages, achieving near-zero waste and allowing significantly more concurrent requests per GPU.

Continuous batching. Rather than waiting for an entire batch to finish before starting new requests, vLLM inserts incoming requests into the running batch at every iteration. This eliminates idle GPU time between requests and keeps throughput consistently high under variable load.

Tensor parallelism. Split a single model across multiple GPUs on the same machine with automatic tensor parallelism. A 70B model that requires more VRAM than a single GPU can provide runs across two or four GPUs with near-linear scaling. Pipeline parallelism extends this across multiple nodes for the largest models.

OpenAI-compatible API. vLLM exposes a production-ready API server that implements the OpenAI chat completions and completions endpoints. Drop it into any application that already uses the OpenAI SDK by changing the base URL.

Broad model support. vLLM supports Llama, Mistral, Mixtral, Qwen, Phi, Gemma, Command R, and many other transformer architectures. It handles GPTQ, AWQ, and FP8 quantized models for reduced memory footprint.

When to Use vLLM

Choose vLLM when throughput and concurrency matter more than ease of setup. It is built for production serving scenarios: internal AI platforms, API services, batch processing pipelines, and any deployment where multiple users or applications share the same model simultaneously. It requires Linux and NVIDIA GPUs with CUDA support.

Ecosystem Role

vLLM occupies the production serving tier of the local AI stack. Where Ollama optimizes for single-user simplicity, vLLM optimizes for multi-user throughput. Pair it with Open WebUI or a custom frontend for interactive use, or call its API directly from application code. For single-user desktop use or quick experimentation, Ollama or LM Studio will be simpler to set up.