[GitHub Trending] AlexsJones/llmfit

7.2 relevance

Hardware-aware model selection tool is highly actionable for engineers running local LLMs, directly relevant to AI infrastructure.

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Why It Matters

For a solutions architect focused on AI/ML agent orchestration and cloud infrastructure, llmfit eliminates guesswork in model selection for local or edge deployments, providing deterministic hardware-to-model matching that can be automated via its REST API and integrated into CI/CD pipelines or multi-agent systems.

Key Takeaways

Use llmfit's benchmark-and-submit workflow to build a hardware-specific model compatibility database for your team, and leverage its REST API to automate model selection in deployment scripts or agent orchestration frameworks like LangGraph or CrewAI.

Hundreds of models & providers. One command to find what runs on your hardware. - AlexsJones/llmfit

Summary

llmfit is an open-source CLI/TUI tool that auto-detects your CPU, RAM, GPU, and VRAM to recommend which LLMs your hardware can actually run, using quantizations like GGUF, AWQ, GPTQ, and EXL2. It includes a new benchmark feature that measures real tokens-per-second on your machine and lets you submit results as a PR directly from the TUI, replacing estimates with measured data for identical hardware configurations. The tool supports multi-platform (macOS, Linux, Windows), multi-GPU setups, MoE architectures, and integrates with local runtimes like Ollama, llama.cpp, MLX, and LM Studio, plus exposes a REST API for orchestrators.

Author

AlexsJones