localfit 1.1.0

Will it fit? GPU toolkit for AI models — MLX + GGUF + Cloud in one menu.

localfit

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Will it fit? Say what model you want — localfit figures out the rest.

Fits locally? Run it via MLX or llama.cpp. Doesn't fit? Kaggle free GPU. Still too big? RunPod cloud. Need a custom quant? Quantize remotely and upload to HuggingFace. You never think about hardware.

pip install localfit

---

Quick Start

localfit                              # GPU dashboard + trending models
localfit run gemma4:e4b               # interactive menu: pick MLX, GGUF, or Cloud
localfit run qwen3:14b                # doesn't fit? menu shows Kaggle/RunPod options
localfit --launch claude              # start model + launch Claude Code
localfit makeitfit llama-4-scout      # quantize remotely → upload to your HuggingFace

The Run Menu

When you localfit run MODEL, you get an interactive menu with arrow key navigation — pick your backend before anything downloads:

╭──────────────────────── Qwen2.5-7B-Instruct ─────────────────────────╮
│   LOCAL                                                              │
│   › MLX   Qwen2.5-7B-Instruct-4bit             3.5GB                │
│     MLX   Qwen2.5-7B-Instruct-8bit             7.0GB  ⭐             │
│     GGUF  Q4_K_M                                4.4GB                │
│     GGUF  Q8_0                                  7.5GB                │
│                                                                      │
│   REMOTE                                                             │
│     Kaggle  T4 16GB                               free               │
│     RunPod  RTX A5000 24GB                    $0.16/hr               │
╰──────────────────────────── Apple Silicon 16GB ──────────────────────╯

• MLX — Native Apple Silicon, fastest on Mac. Auto-discovers mlx-community models.
• GGUF — llama.cpp with Metal/CUDA. Works everywhere.
• Kaggle — Free 30h/week GPU. One click to deploy.
• RunPod — Paid cloud GPU. Auto-picks cheapest that fits.

Backends

MLX (Apple Silicon)

localfit auto-detects if you have mlx-lm installed and finds MLX models on HuggingFace:

pip install mlx-lm                    # one-time setup
localfit run gemma-3-4b-it            # auto-picks mlx-community/gemma-3-4b-it-8bit

If no mlx-community model exists, localfit can convert any HuggingFace model to MLX locally:

localfit run bytedance-research/UI-TARS-7B-DPO
# → No mlx-community model found
# → "Convert locally? mlx_lm needs ~14GB RAM (you have 24GB)"
# → Converts to MLX 4-bit → serves immediately

GGUF (llama.cpp)

The default for all platforms. localfit downloads the best GGUF quant for your GPU and serves via llama-server:

localfit run gemma4:26b               # MoE, 12GB, best quality on 24GB Mac
localfit show gemma4:26b              # show all quants + fit analysis + cloud pricing

Remote Kaggle (Free)

30 hours/week of free T4 GPU. localfit auto-deploys via Cloudflare tunnel:

localfit run qwen3:14b --remote kaggle
localfit --remote-status              # check active session
localfit --remote-stop                # stop + free quota

Remote RunPod (Paid)

Any GPU size. Live pricing from the API. Auto-stop when budget runs out:

localfit login runpod                 # save API key
localfit run gemma4:27b --cloud       # auto-provision + tunnel
localfit --stop                       # kill pod + stop billing

Make It Fit — Remote Quantization

Can't find the right quant? Create your own and upload to HuggingFace:

localfit makeitfit Qwen2.5-7B-Instruct

  Your GPU: Apple Silicon 16GB
  Model: Qwen/Qwen2.5-7B-Instruct (14GB BF16)

  1  Quantize on Kaggle (free) → Q4_K_M GGUF     ~7 min
  2  Quantize on RunPod         → Q5_K_M GGUF     ~$0.10
  3  Serve remotely (no quant)

  Pick option:

How it works:

1. Picks Kaggle GPU (free) or RunPod (cheapest available)
2. Downloads model from HuggingFace
3. Converts to F16 GGUF via llama.cpp
4. Quantizes to your chosen method (Q4_K_M, Q5_K_M, Q8_0, etc.)
5. Uploads to your HuggingFace repo
6. Run it: localfit run yourname/model-Q4_K_M-GGUF-localfit

Uses llama.cpp native tools — no Unsloth dependency, works reliably on Kaggle and RunPod.

Launch Any Tool

Start a model and launch your coding tool in one command:

localfit --launch claude              # Claude Code
localfit --launch claude --model gemma4:26b
localfit --launch codex               # OpenAI Codex CLI
localfit --launch opencode            # OpenCode
localfit --launch aider               # aider
localfit --launch webui               # Open WebUI (ChatGPT-style browser)
localfit --launch webui --tunnel      # + public URL via Cloudflare

Works with both local and remote models. Env vars are scoped to the subprocess only — your normal tool setup is never touched.

All Commands

Model Management

localfit run MODEL                    # interactive menu → pick backend → serve
localfit run MODEL --remote kaggle    # serve on free Kaggle GPU
localfit run MODEL --cloud            # serve on RunPod (paid)
localfit pull MODEL                   # download only
localfit list                         # installed models
localfit ps                           # running models
localfit stop                         # stop local server
localfit show MODEL                   # all quants + fit analysis + pricing

Quantization

localfit makeitfit MODEL              # quantize remotely → upload to HuggingFace
localfit login huggingface            # save HF write token (for uploads)

GPU & Hardware

localfit                              # GPU dashboard + trending models
localfit health                       # GPU VRAM, temp, processes
localfit specs                        # full machine specs
localfit simulate                     # interactive "will this model fit?"
localfit bench                        # benchmark installed models
localfit arena                        # leaderboard on YOUR hardware
localfit trending                     # top models with fit/cloud tags

Tool Integration

localfit --launch TOOL                # start model + launch tool
localfit --config TOOL                # show safe launch command
localfit doctor                       # check all tool configs
localfit restore                      # restore configs from backup

Cloud & Remote

localfit login kaggle                 # save Kaggle credentials
localfit login runpod                 # save RunPod API key
localfit login huggingface            # save HF token
localfit --remote-status              # check active Kaggle session
localfit --remote-stop                # stop Kaggle session
localfit --stop                       # stop RunPod pod

System

localfit check                        # check prerequisites (llama-server, CUDA, etc.)
localfit cleanup                      # free GPU memory
localfit debloat                      # disable macOS services stealing GPU

Supported Platforms

Platform	GPU Detection	Backends
macOS Apple Silicon	Metal	MLX + llama.cpp + Ollama
Linux NVIDIA	CUDA (nvidia-smi)	llama.cpp + Ollama
Linux AMD	ROCm (rocm-smi)	llama.cpp + Ollama
Windows (WSL2)	CUDA (nvidia-smi)	llama.cpp + Ollama

Dynamic VRAM Context Sizing

localfit auto-calculates the optimal context window:

Machine	Model	Context
M4 Pro 24GB	Gemma 4 26B (12GB)	32K
M4 Pro 24GB	Gemma 4 E4B (4.6GB)	128K
M4 Max 64GB	Gemma 4 26B (12GB)	128K

Cloud Setup

Kaggle (Free)

# 1. Get your Legacy API Key at https://www.kaggle.com/settings
#    → "Legacy API Credentials" → "Create Legacy API Key" → downloads kaggle.json
# 2. Save it:
localfit login kaggle
# 3. Run any model:
localfit run gemma4:e4b --remote kaggle

RunPod (Paid)

# 1. Get API key at https://www.runpod.io/console/user/settings
# 2. Save it:
localfit login runpod
# 3. Run any model:
localfit run gemma4:27b --cloud

HuggingFace (For Uploads)

# 1. Create a write token at https://huggingface.co/settings/tokens
# 2. Save it:
localfit login huggingface
# 3. Quantize + upload:
localfit makeitfit Qwen2.5-7B-Instruct

Requirements

• Python 3.10+
• llama.cpp or Ollama (auto-installed)
• Optional: mlx-lm for Apple Silicon MLX backend

pip install localfit                  # core
pip install 'localfit[all]'           # + TUI dashboard + HF downloads
pip install mlx-lm                    # + MLX backend (Mac only)

License

Apache-2.0