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 # Interactive TUI (9 modes)
fitmyllm chat <model> # Chat directly with a model
fitmyllm benchmark # Run a speed benchmark
fitmyllm my-benchmarks # View your submitted benchmarks
fitmyllm telemetry on|off # Toggle anonymous speed telemetry
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 community comparison |
Live Speed Metrics
Chat shows real-time tok/s during streaming and a summary after each response:
42.3 tok/s · 210ms TTFT · 156 tokens
Community Speed Telemetry
When opted in (fitmyllm telemetry on), the CLI silently collects anonymous speed metrics (tok/s, TTFT) during chat sessions and uploads them to improve predictions. No message content is ever sent.
Community speed data feeds back into the CLI and the web UI:
- Find Models detail panel:
Community 42 tok/s (12 reports)alongside predicted speed - Model Detail: per-quant breakdown with median, range, and report count
- Benchmark results: your speed vs community median comparison
- Web model pages: community speed section on fitmyllm.com model detail pages
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, telemetry opt-in
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)
1 maintainer
Metadata
1 maintainer
Release files for fitmyllm 0.3.110
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fitmyllm-0.3.110.tar.gz | 77.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fitmyllm-0.3.110-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 181.3 kB
Release files / fitmyllm-0.3.110.tar.gz
| Download URL | fitmyllm-0.3.110.tar.gz |
|---|---|
| Size | 77.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
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Release files / fitmyllm-0.3.110-py3-none-any.whl
| Download URL | fitmyllm-0.3.110-py3-none-any.whl |
|---|---|
| Size | 104.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
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