Find the Best Local AI Model for Your Device
Tell us your hardware and get the single best local LLM it can actually run: one verified pick sized to your exact memory, not a generic chatbot guess. Honest speed and quality estimates for Apple Silicon, iPhone, NVIDIA, and AMD.
Or jump to any device, try: RTX 4090, MacBook Pro M5, 16GB, iPhone 17 Pro
Install the optimized LLM for your machine in one line
ModelFit's free, zero-dependency CLI detects your exact hardware, picks the single best local model it can run, and hands you the one-line Ollama command to install it. The dataset behind it is open (CC BY 4.0), and AI agents can query it live over MCP.
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How much LLM fits your memory?
A local LLM needs roughly 0.6 GB per billion parameters at Q4. ModelFit sizes picks to ~70% of your unified memory on machines up to 32GB, scaling to ~85% at 128GB+, leaving room for the OS, context, and KV-cache. Here is what each tier comfortably runs today. The same math applies to GPU VRAM: for dedicated cards, see the per-card GPU guides.
| Unified memory | Runs up to | Top local pick | Models that fit |
|---|---|---|---|
| 8 GB | ~8.3B params | LFM2.5 8B-A1B | 23 of 75 |
| 12 GB | ~12B params | Gemma 4 12B | 29 of 75 |
| 16 GB | ~14B params | Qwen3.5 9B Instruct (Q8) | 37 of 75 |
| 24 GB | ~27B params | Gemma 4 12B (Q8) | 45 of 75 |
| 32 GB | ~35B params | Qwen3.6 35B-A3B | 54 of 75 |
| 36 GB | ~35B params | Qwen3.6 35B-A3B | 56 of 75 |
| 48 GB | ~46.7B params | Qwen3.6 27B (Q8) | 59 of 75 |
| 64 GB | ~70B params | Qwen3.6 35B-A3B (Q8) | 64 of 75 |
| 72 GB | ~80B params | Qwen3.6 35B-A3B (Q8) | 65 of 75 |
| 96 GB | ~122B params | Qwen3.5 122B-A10B Instruct | 70 of 75 |
| 128 GB | ~122B params | Qwen3.5 122B-A10B Instruct | 71 of 75 |
| 192 GB | ~235B params | Qwen3 235B A22B | 72 of 75 |
| 256 GB | ~235B params | Qwen3 235B A22B | 72 of 75 |
Derived from ModelFit's own catalog of 107 models · dataset updated 2026-07-06 · full dataset (CC BY 4.0)
Local vs Cloud: How Close Are We?
Open-weight models you can run at home keep narrowing the gap with the best closed APIs on coding. Here is where the line sits today on SWE-Bench Verified.
SWE-Bench Verified · third-party scores, each raw-confirmed against the model's primary source. Qwen3.6-27B runs locally on a 24GB Mac.
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Local LLM questions, answered
How much memory do I need to run a local LLM?
A local model needs roughly 0.6 GB of memory per billion parameters at Q4 quantization. ModelFit sizes its picks to about 70% of your device's unified memory (scaling to ~85% on 128GB+ machines), so an 8GB machine comfortably runs models up to ~8.3B and a 16GB machine up to ~14B parameters.
What is the best local LLM for a 16GB Mac?
On a 16GB Mac, ModelFit's top pick is Qwen3.5 9B Instruct (Q8): it loads in about 10.7GB and is the highest-quality local model that fits comfortably. 37 of ModelFit's 75 local models fit a 16GB device.
Can I run these models completely offline?
Yes. Every model ModelFit recommends runs fully offline through Ollama on your own hardware: no API keys, no subscription, and no data leaves your machine.
How many AI models does ModelFit cover?
ModelFit tracks 107 AI models across 21 families; 75 run locally via Ollama on Apple Silicon, NVIDIA, or AMD hardware.
How accurate are the speed estimates?
Tokens-per-second figures are ModelFit estimates derived from your chip or GPU's memory bandwidth and the model's size, not measured benchmarks. Real-world speed varies with quantization, context length, and thermal state.