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llmfit

What it is

llmfit is a hardware-to-model fit utility that helps you determine which models and providers are realistic for your machine.

What problem it solves

It prevents wasted time trying to run models that do not fit your hardware or performance requirements.

Where it fits in the stack

Development & Ops / Model Selection Utility. It is a planning tool for local AI deployment decisions.

Typical use cases

  • Choosing models for local inference
  • Comparing what can run on different hardware profiles
  • Deciding whether to use LocalAI, Ollama, or a cloud provider

Strengths

  • Fast Hardware Reality Check: Instantly detects CPU, RAM, and GPU/VRAM to provide tailored model recommendations.
  • Vim-like TUI: Powerful interactive interface with search, filtering, and bulk comparison modes.
  • Community Benchmarks: Integration with localmaxxing.com (press b) to see real-world performance data from other users.
  • Hardware Simulation: Press S to override your system specs and see what models would run on a target upgrade (e.g., RTX 5090).
  • Download Manager: Native management of model downloads and local cache for Ollama, llama.cpp, and LM Studio.

Limitations

  • Estimation vs. Execution: Provides theoretical speed and fit estimates; actual performance may vary based on concurrent system load.
  • Workflow Agnostic: Helps with feasibility and fit, but does not design the application-level workflow or agent architecture.

When to use it

  • Before investing in new hardware for local LLM execution.
  • When choosing the optimal quantization level for a specific model on your machine.
  • To compare real-world performance data from the community before downloading large models.

Getting started

Installation

macOS / Linux (Homebrew) 

brew install llmfit

Python (uv / pip) 

uv tool install -U llmfit
# or
pip install llmfit

Quick Install (Script) 

curl -fsSL https://llmfit.axjns.dev/install.sh | sh

Initial Run

Simply type llmfit to launch the interactive TUI. It will automatically detect your CPU, RAM, and GPU/VRAM to provide tailored recommendations.

CLI and TUI examples

Interactive TUI (Default)

llmfit
- Navigation: j/k or arrows. - Search: / to search by name, provider, or use case. - Filters: f (fit), a (availability), R (runtime). - Leaderboard: b to view community benchmarks. - Plan Mode: p to calculate hardware requirements for a specific model.

System Audit

# Display detected system hardware specs in JSON format
llmfit system --json

Model Recommendations

# Get top 5 recommendations for coding in JSON format
llmfit recommend --use-case coding --limit 5 --json

Hardware Planning

# Estimate required hardware for a specific model and context length
llmfit plan "meta-llama/Llama-3.1-8B" --context 8192 --json

API and Integration

llmfit can run as a background service to provide fit data via a REST API or integrate directly as an OpenClaw Skill.

Starting the Server

llmfit serve --host 0.0.0.0 --port 8787

Fetching Node Recommendations

import requests

# Query the local llmfit service for the best coding models
url = "http://localhost:8787/api/v1/models/top?limit=3&use_case=coding"
response = requests.get(url)
models = response.json()

for model in models:
    print(f"Recommended: {model['name']} (Score: {model['score']})")

When not to use it

  • When you already know you will use hosted frontier APIs (OpenAI, Anthropic, etc.) and have no interest in local execution.
  • If you require a tool that actually benchmarks the model on your hardware by running it (see llm-checker).

Sources / References

Contribution Metadata

  • Last reviewed: 2026-06-06
  • Confidence: high