vLLM
Running a large language model efficiently at scale is a genuinely hard engineering problem — vLLM is the open-source engine that many AI companies quietly rely on to serve LLMs fast and cheaply, without you having to solve GPU memory management yourself.
🔗 Visit vLLM
Description
Serving a large language model to real users is expensive and technically tricky: naive approaches waste huge amounts of GPU memory and can't handle many simultaneous requests efficiently. vLLM is an open-source inference engine, originally developed at UC Berkeley, built specifically to solve this — it lets you deploy LLMs that respond faster and serve more users per GPU than most alternative setups.
Its core innovation, PagedAttention, manages GPU memory the way an operating system manages RAM, enabling continuous request batching and prefix caching. It exposes an OpenAI-compatible API, supports 200+ model architectures, multiple quantization formats (FP8, INT8, INT4, GPTQ, AWQ, GGUF), speculative decoding, multi-LoRA and structured output generation, and works across a broad range of hardware. It's entirely free and open-source under Apache-2.0, with 2,000+ active contributors and adoption by major AI organizations in production.
💬 Our review
The short version: if you're deploying open-source LLMs yourself rather than only calling a hosted API, vLLM is close to the default choice for serious production serving — the performance gains from PagedAttention are real and well-documented.
Against Ollama (much simpler, great for local/desktop use but not built for high-throughput production serving) or TensorRT-LLM (NVIDIA-specific, can be faster on their hardware but far more complex to set up), vLLM's edge is broad hardware support and OpenAI-API compatibility combined with genuinely production-grade throughput. There's no cost to worry about since it's fully open-source, but you do need real infrastructure and GPU expertise to run it well — this isn't a beginner tool. Worth it for any team self-hosting LLMs at meaningful scale; overkill if you just want to run a small model on your laptop (Ollama is the better fit there).
💰 Pricing
📊 Global score
🤖 AI-enriched data
Entièrement gratuit et open-source (Apache-2.0), financé par dons
Pros
PagedAttention — gestion mémoire GPU très efficace
API compatible OpenAI
Support de 200+ architectures de modèles
2000+ contributeurs actifs, adopté en production par de grandes organisations
Cons
Nécessite une vraie expertise GPU/infra pour bien l'exploiter
Pas adapté à un simple usage local sur laptop (Ollama est plus simple)
Configuration plus complexe que les solutions tout-en-un