vLLM
A high-throughput open-source LLM inference engine — pioneered PagedAttention to manage KV cache like virtual memory, dramatically improving GPU utilization for serving open models.
vLLM (UC Berkeley, 2023) is the most-deployed open-source LLM serving stack in 2026. Its key innovation, PagedAttention, treats the KV cache like OS-style paged memory — many requests share a GPU efficiently, batching dynamically as they arrive.
Compared to naive Transformers serving, vLLM delivers 5–24× higher throughput at similar latency on the same hardware. It supports Llama, Mistral, Qwen, DeepSeek, and most open-source families, with quantization, LoRA, and speculative decoding baked in.
For teams self-hosting open-source models behind agents, vLLM is the default 2026 choice. The alternatives — SGLang, TGI, TensorRT-LLM — have specific advantages but vLLM is the broadest-compatibility option.
Frequently asked
vLLM vs. SGLang — which should I pick?+
vLLM for broad compatibility and the largest community. SGLang for highest throughput on structured outputs and constrained decoding. Most teams start with vLLM and only switch when they hit a specific limit.
Can vLLM run frontier models like Claude or GPT?+
No — vLLM runs open-weight models. For proprietary frontier models you use the vendor's API. vLLM's niche is self-hosted Llama, Qwen, DeepSeek, Mistral, etc.