llmfit Answers the First Local-Model Question: Will It Run?
On GitHub trending today, up 239 stars to 32.2k: llmfit by Alex Jones, a Rust terminal tool, MIT licensed, that answers the question every local-AI conversation starts with, given this RAM, CPU and GPU, which models can I actually run, at what quantization, and how well. It scores candidates across quality, speed, fit and context, handles multi-GPU setups and MoE architectures, does dynamic quantization selection, and plugs into the runtimes people actually use, Ollama, llama.cpp, MLX, Docker Model Runner. TUI and CLI modes, Python bindings included. github.com/AlexsJones/llmfit
The recent addition that makes it more than a calculator: community benchmarking from inside the TUI. You can download a model, serve it, measure real throughput on your hardware, and submit the result back to the project as a pull request without leaving the interface. That is the missing dataset in local AI, everyone currently guesses from parameter counts and vendor marketing, and the gap between theoretical fit and actual tokens per second is where all the disappointment lives. Crowdsourced, hardware-specific, verified measurements attack exactly that gap.
The timing explains the star velocity. With 27B-class open models suddenly genuinely competitive, Qwen3.8-27B posting frontier-adjacent agentic scores, GLM-5.3 leading coding benchmarks, "will it run on my machine" stopped being a hobbyist question and became a procurement question. The answer is genuinely nontrivial, quantization times context length times MoE offload is a three-dimensional tradeoff, and a tool that turns it into one command was overdue.
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The recent addition that makes it more than a calculator: community benchmarking from inside the TUI. You can download a model, serve it, measure real throughput on your hardware, and submit the result back to the project as a pull request without leaving the interface. That is the missing dataset in local AI, everyone currently guesses from parameter counts and vendor marketing, and the gap between theoretical fit and actual tokens per second is where all the disappointment lives. Crowdsourced, hardware-specific, verified measurements attack exactly that gap.
The timing explains the star velocity. With 27B-class open models suddenly genuinely competitive, Qwen3.8-27B posting frontier-adjacent agentic scores, GLM-5.3 leading coding benchmarks, "will it run on my machine" stopped being a hobbyist question and became a procurement question. The answer is genuinely nontrivial, quantization times context length times MoE offload is a three-dimensional tradeoff, and a tool that turns it into one command was overdue.
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