vLLMdefinition and how it works in 2026
- vLLM
- A high-throughput open-source LLM inference engine โ 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 โ many requests share a GPU efficiently, batching dynamically as they arrive.
Compared to naive Transformers serving, vLLM delivers 5โ24ร 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 โ SGLang, TGI, TensorRT-LLM โ have specific advantages but vLLM is the broadest-compatibility option.
Frequently asked
vLLM vs. SGLang โ 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 โ 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.