Comparison · Last reviewed

Ollama vs llamay

Ollama and llamay both run GGUF models on your own machine behind a local HTTP API, and llamay reads the models Ollama has already downloaded, so trying one beside the other costs nothing. Choose Ollama for an MIT-licensed open-source project, GPU acceleration on Windows and AMD cards, and Modelfiles. Choose llamay when the computed state of a long prompt has to be forked, saved to a file and restored, or when you want Ollama's models served with no change to your clients.

Side by side

Ollamallamay
LicenceMIT, open source — LICENSEProprietary, free to download and run — pricing
Runs onmacOS 14 or later (GPU on Apple M-series, CPU only on Intel Macs) — macOS docs; Windows and LinuxmacOS (Metal on Apple silicon; the CPU on Intel Macs), Linux (CUDA or Vulkan in the GPU build, the CPU otherwise), Windows (the CPU) and Android (the command line) — backends
GPUsNVIDIA (CUDA), AMD (ROCm on Linux and Windows), Apple Metal, and Vulkan on Windows and Linux — hardware supportApple Metal; NVIDIA CUDA and Vulkan in the Linux GPU build. The Windows builds are CPU only — backends
Default port11434 — OpenAI compatibility11435 — serving
OpenAI APIYes: /v1/chat/completions, /v1/completions, /v1/embeddings, /v1/responses and more — OpenAI compatibilityYes: /v1/chat/completions, /v1/completions, /v1/responses, /v1/embeddings — HTTP API
Anthropic APIYes, a subset of /v1/messages; tool_choice and prompt caching are listed as unsupported — Anthropic compatibilityYes, /v1/messages with tools, tool_choice and streaming — migrating
Ollama APINative/api/chat, /api/generate, /api/tags, /api/pull — Ollama clients work unchanged
ModelsIts own library and store; a local GGUF is imported with a Modelfile (FROM /path/to/file.gguf) — importingIts own store, plus Ollama's, read in place; pulls from Ollama's registry, hf.co/… or any URL — getting models
Default context lengthSet by GPU memory: 4k under 24 GiB, 32k from 24 to 48 GiB, 256k above — context lengthThe model's own, read from the file — migrating
Customising a modelModelfile — Modelfile referenceNo Modelfile; the system prompt goes in the request, or in a context you fork
Desktop appYes, on macOS — macOS docsMac app, Windows and Linux windows, and Studio in the browser — the apps

Which to choose

Choose Ollama if

  • you want the engine to be open source: Ollama is MIT-licensed and llamay is not;
  • you have an AMD card, or an NVIDIA card on Windows — Ollama accelerates those, and llamay's Windows builds run on the CPU;
  • you build and share customised models as Modelfiles.

Choose llamay if

  • many requests share one long beginning — a manual, a codebase, an agent's instructions — and you want that work done once, forked per request, and kept on disk between runs;
  • you want the models Ollama already downloaded served by a second engine, with no re-download and no import;
  • you want a model's full context length by default rather than one set by how much GPU memory the machine has;
  • your Anthropic-API client sends tool_choice, or you want JSON Schema output enforced while sampling.

Try them side by side

Both can run at once. Ollama keeps port 11434, llamay takes 11435, and llamay reads the models Ollama has already pulled, so this costs no downloads:

  1. curl -fsSL https://llamay.com/install.sh | sh
  2. llamay run -m llama3.2:3b -p "hello" — a model Ollama already has
  3. llamay serve, then point a client at http://localhost:11435/v1

Speed

We do not publish an Ollama benchmark, and you should distrust any page that gives one number for both: the result moves with the model file, the quantisation, the build and the hardware far more than with the engine's name. Measure on your own machine — llamay bench -m <model> reports prefill and decode separately — and read Throughput for what moves each one.

Questions

Can llamay use the models I downloaded with Ollama?

Yes. llamay reads Ollama's model store in place, so a model Ollama has pulled is served by name with no re-download, no conversion and no import step. llamay never writes into Ollama's store.

Can Ollama and llamay run at the same time?

Yes. Ollama listens on port 11434 and llamay on 11435, so both can run on one machine against the same weights and you can point one client at each.

Is llamay open source like Ollama?

No. Ollama is MIT-licensed. llamay is proprietary software from Kosa8 Labs: free to download and run on your own machine, with paid hosted plans.

Does Ollama support the Anthropic API?

Yes. Ollama's documentation describes a subset of the Anthropic Messages API at /v1/messages, and lists tool_choice and prompt caching among what it does not support. llamay serves /v1/messages including tool_choice.

Which is faster, Ollama or llamay?

It depends on the model file, the quantisation, the build and the hardware, so measure on your machine. llamay bench reports prefill and decode separately; we do not publish a number for Ollama.

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. Ollama — LICENSE (MIT)
  2. Ollama docs — Ollama for macOS
  3. Ollama docs — Hardware support
  4. Ollama docs — OpenAI compatibility
  5. Ollama docs — Anthropic compatibility
  6. Ollama docs — Importing a model
  7. Ollama docs — Context length
  8. Ollama docs — Modelfile reference

Last reviewed .