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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Hundreds of models & providers. One command to find what runs on your hardware.
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| 2026-07-31 | 30956 |
| 2026-08-01 | 30956 |
| 2026-08-05 | 31151 |
| 2026-08-06 | 31151 |
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# llmfit <p align="center"> <img src="assets/icon.svg" alt="llmfit icon" width="128" height="128"> </p> <p align="center"> <b>English</b> · <a href="README.zh.md">中文</a> · <a href="README.ja.md">日本語</a> </p> <p align="center"> <a href="https://github.com/AlexsJones/llmfit/actions/workflows/ci.yml"><img src="https://github.com/AlexsJones/llmfit/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://crates.io/crates/llmfit"><img src="https://img.shields.io/crates/v/llmfit.svg" alt="Crates.io"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> <a href="https://about.signpath.io"><img src="https://img.shields.io/badge/SignPath-signed-brightgreen?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgZmlsbD0id2hpdGUiIHZpZXdCb3g9IjAgMCAxNiAxNiI+PHBhdGggZD0iTTEwLjA2NyA0LjU2N2wtNC43MzQgNC43MzMtMS40LTEuNGExIDEgMCAwIDAtMS40MTQgMS40MTRsMi4xIDIuMWExIDEgMCAwIDAgMS40MTQgMGw1LjQ0LTUuNDRhMSAxIDAgMCAwLTEuNDE0LTEuNDE0eiIvPjwvc3ZnPg==" alt="Signed with SignPath"></a> </p> > **📊 New: benchmark & share — real numbers from your machine, better estimates for everyone.** Download a model, serve it, and measure real tok/s on your hardware — then contribute the results back to the project as a PR, straight from the TUI. No `gh` CLI, no third-party account. Every run is saved locally first, your own measurements replace estimates in the fit table, and each merged submission ships in the next release: anyone on identical hardware gets measured `✓` numbers before they ever run a benchmark. [Follow the step-by-step benchmarking guide →](docs/benchmarking.md) > > *Previously: [llmfit 1.0 — the release where the numbers became verifiable →](https://github.com/AlexsJones/llmfit/discussions/708)* **Hundreds of models & providers. One command to find what runs on your hardware.** A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine. Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio). > **Sister projects:** > - [sympozium](https://github.com/sympozium-ai/sympozium/) — managing agents in Kubernetes. > - [llmserve](https://github.com/AlexsJones/llmserve) — a simple TUI for serving local LLM models. Pick a model, pick a backend, serve it. > - [llama-panel](https://github.com/AlexsJones/llama-panel) — a native macOS app for managing local llama-server instances.  ## Documentation | | | |---|---| | **Get started** | [Install](#install) · [Usage](#usage) · [How it works](#how-it-works) | | **Guides** | [TUI guide](docs/tui.md) · [Benchmarking step-by-step](docs/benchmarking.md) · [CLI & automation](docs/cli.md) · [Runtime providers](docs/providers.md) · [OpenClaw integration](docs/openclaw.md) | | **Reference** | [How it works (full)](docs/how-it-works.md) · [Platform & GPU support](docs/platform-support.md) · [Custom models](docs/custom-models.md) · [Development](docs/development.md) | | **Project** | [Contributing](#contributing) · [Alternatives](#alternatives) · [Code signing](#code-signing) · [License](#license) | --- ## Install ### Windows ```sh scoop install llmfit ``` If Scoop is not installed, follow the [Scoop installation guide](https://scoop.sh/). ### macOS / Linux #### Homebrew Prebuilt binary (recommended, works on all macOS/Linux versions): ```sh brew install AlexsJones/llmfit/llmfit ``` Or from the homebrew-core formula, which builds from source on macOS versions without a bottle: ```sh brew install llmfit ``` #### MacPorts ```sh port install llmfit ``` #### Quick install ```sh curl -fsSL https://l
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ca94cef4f0be1476, topic:llm