Comparison · Last reviewed

vLLM vs llamay

vLLM is an Apache-2.0 Python serving engine for data-centre GPUs, built around PagedAttention and continuous batching to serve many users at once; llamay is one binary for running GGUF models on the machine in front of you. Choose vLLM to serve a team or a product from NVIDIA, AMD or Intel GPUs on Linux. Choose llamay for a laptop, desktop or single server, on macOS, Windows or Linux, with GGUF files and contexts you can save and restore.

Side by side

vLLMllamay
LicenceApache-2.0, open source — LICENSEProprietary, free to download and run — pricing
Operating systemLinux; Windows is not supported natively (WSL instead); Python 3.11 to 3.14 — GPU installationmacOS, Windows, Linux and Android; no runtime to install — install
HardwareNVIDIA CUDA, AMD ROCm, Intel XPU, and CPUs; Apple silicon GPUs through the separate vLLM-Metal project — installationCPU, Apple Metal, CUDA and Vulkan — backends
Built forThroughput: PagedAttention, continuous batching, chunked prefill, prefix caching — READMEReusing computed state on one machine: contexts that fork, snapshot and restore — state engine
APIsOpenAI-compatible (chat, completions, responses, embeddings) and Anthropic /v1/messages — online servingOpenAI, Anthropic and Ollama — HTTP API
Prefix cachingAutomatic prefix caching reuses a shared prefix's KV cache — APCAlways on, over a radix tree of prefixes; contexts can also be named, forked and written to disk — contexts
GGUF"Highly experimental and under-optimized", now through the vllm-gguf-plugin — GGUF docsThe native format — models
Structured outputxgrammar or guidance — READMEJSON Schema enforced while sampling, and GBNF — structured output

Which to choose

Choose vLLM if

  • you serve many concurrent users from data-centre GPUs and throughput per card is the number that matters;
  • your models are Hugging Face safetensors checkpoints rather than GGUF files;
  • you need tensor parallelism across several GPUs;
  • you want an open-source engine under Apache-2.0.

Choose llamay if

  • the model runs on a laptop, a desktop, or one server — including a Mac or a Windows machine, where vLLM does not run natively;
  • your models are GGUF files, or already in Ollama's store;
  • you want one binary with no Python environment to keep working;
  • a long prompt's computed state should be saved to a file and restored later, or on another machine.

Questions

Does vLLM run on Windows or macOS?

vLLM's GPU installation docs list Linux as the operating system and say Windows is not supported natively, suggesting WSL. Apple silicon GPUs are reached through the separate vLLM-Metal project. llamay runs natively on macOS, Windows and Linux.

Can vLLM run GGUF models?

Its documentation calls GGUF support highly experimental and under-optimized, and it now needs the vllm-gguf-plugin. GGUF is llamay's native format.

Does vLLM have prefix caching?

Yes. vLLM's automatic prefix caching reuses the KV cache of a shared prefix. llamay's prefix cache is always on, and its contexts can also be forked and saved to a file.

Is llamay a replacement for vLLM in production?

Not for serving many users from a GPU cluster, which is what vLLM is built for. llamay is built for one machine: a laptop, a workstation or a single server.

Sources

Every statement above about another project is taken from that project's own documentation, repository or licence, read on 31 October 2026. Anything we could not confirm there is left out. If one of these has changed, write to [email protected] and we will correct the page.

  1. vLLM — LICENSE (Apache-2.0)
  2. vLLM — README
  3. vLLM docs — Installation
  4. vLLM docs — GPU installation requirements
  5. vLLM docs — Online serving (OpenAI and Anthropic APIs)
  6. vLLM docs — Automatic prefix caching
  7. vLLM docs — GGUF

Last reviewed .