vLLM
vLLM is a library that optimizes memory management with PagedAttention technology, developed for the extraction and presentation of large language models. It aims to use hardware resources efficiently with continuous batch processing, various quantization methods and distributed inference support. OpenAI and Anthropic-compatible API can work in a wide range of hardware with different model architectures.
vLLM
95vllm-project · Local AI / Self-Hosted LLM
Common commercial products in this category: ChatGPT, Claude, Gemini
- You can serve over 200 model architectures on Hugging Face via OpenAI or Anthropic-compatible API.
- Using PagedAttention and continuous batch processing techniques, you can manage high-volume inference processes.
- With methods such as tensor, pipeline, and expert parallelism, you can run large models by distributing them to different hardware platforms.
- • High number of open issues (9,644)
- • NeutralHacker News
vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention
“Yes, vLLM focuses on maximizing throughput when the VRAM is fully utilized. Nevertheless, I believe users can still benefit from vLLM even if they don't utilize the memory to its full capacity, because vLLM also includes other optimizations orthogonal to the PagedAttention…”
- • NeutralHacker News
vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention
“This really depends on what GPUs you use. If you GPUs has very small amount of memory, vLLM will help more. vLLM addresses the memory bottleneck for saving KV caches and hence increases the throughput.”
Verified against README · Oct 26, 2026 · source
My note
Alternative pages
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