fitmyllm 0.3.31

Find the best local AI model for your GPU — terminal UI

fitmyllm

Run the right LLM locally. Automatically.

Install

pip install fitmyllm

Or run without installing:

pipx run fitmyllm

Setup

Get your free API key at fitmyllm.com/?tab=mcp, then:

fitmyllm setup
# Paste your API key (starts with fml_)

Or set it as an environment variable:

export FITMYLLM_API_KEY=fml_your_key_here

Run

fitmyllm

Features

Main screens

Screen	Description
Quick Run	Zero-config: detect GPU → recommend best model → download GGUF → start server → chat. No decisions needed
Find Models	Auto-detect GPU, 18+ filters (use case, context, size, family, quant, speed, KV cache, capabilities, 14 benchmark minimums, 19 sort options including per-benchmark ranking), multi-GPU support
Find GPU	GPU recommendations for any model with budget, speed, vendor, and quant filters
Enterprise	10-tab deployment analysis: overview, risk, checklist, TCO, scaling, SLA, GPU matrix, performance, fine-tuning, architecture
Model Library	Browse all installed models from every backend (Ollama, llama-server, local GGUF). Chat, delete, disk usage
Tier List	Models and GPUs ranked S-F with cloud GPU alternatives
Benchmarks	Leaderboard sortable by 8 benchmark metrics
GPU Prices	Search and compare GPU pricing with vendor filter
Run Benchmark	Select from installed/recommended models, backend-agnostic speed test with delta vs predicted speed

Available from within screens

Feature	Access	Description
Compare	Space to mark, c to compare	Side-by-side comparison of up to 4 models with all metrics
Install	i on any model	Choose quantization, pick engine (8 supported), or download GGUF from HuggingFace with progress bar
Chat	c from Model Library	Talk to models via any backend with real-time streaming and collapsible thinking blocks
Charts	v from Find Models	ASCII score/speed/VRAM bars and quality-vs-speed scatter plot
Command Simulator	t from model detail	Interactive parameter tuning for 8 engines (context, batch size, KV quant, GPU layers)
Export	e from Find Models	Export results as Markdown

Multi-Backend Support

The CLI auto-detects running inference backends and works with any of them:

Backend	Port	Notes
Ollama	11434	Full support: pull, run, chat, model listing
llama-server	8080	llama.cpp HTTP server — auto-started or manual
OpenAI-compatible	8080	vLLM, LM Studio, or any /v1/chat/completions server

Quick Run can auto-start llama-server with optimal parameters (GPU layers, context length, batch size) calculated from your hardware.

GGUF Model Management

Download and manage GGUF models without Ollama:

• Download from any HuggingFace repo by quantization level
• Inventory tracked in ~/.fitmyllm/models/inventory.json
• Storage in ~/.fitmyllm/models/ (configurable)
• No extra dependencies — uses httpx for downloads

Keyboard Shortcuts

Key	Action
f	Toggle filter panel
g	Search/change GPU
Space	Mark model for comparison
c	Compare marked models / Chat from library
d	Delete model (in Model Library)
i	Install model
m	Manual input (in Run Benchmark)
t	Command simulator / Toggle thinking
s	Save/unsave model
r	Refresh / Show HuggingFace README
e	Export results as Markdown
v	Show ASCII charts
Ctrl+S	Save current filters as defaults
Ctrl+T	Toggle thinking blocks in chat
Esc	Go back
q	Quit

Supported Engines

Ollama, llama-server, vLLM, LM Studio, llama.cpp, KoboldCpp, Jan, Docker Model Runner

Data Storage

~/.fitmyllm/
  config.json     Preferences, API key, saved models, backend preference
  cache/          API response cache (24h TTL, offline fallback)
  models/         Downloaded GGUF files + inventory.json

Requirements

• Python 3.10+
• API key from fitmyllm.com
• Ollama or llama-server (optional — for chat/benchmark features)