Local LLM Compatibility Dataset
107 models by params, quantization, and memory load. Which run locally on Apple Silicon and NVIDIA GPUs. Free under CC BY 4.0. Updated 2026-07-25.
This is ModelFit's open compatibility dataset: every model the site tracks, with the parameter size, quantization, minimum RAM, and estimated memory load used to decide what runs locally. Memory figures are system/unified RAM; the same budget math applies to GPU VRAM (per-card breakdowns live in the GPU guides). Reuse it freely with attribution (CC BY 4.0). Credit ModelFit (modelfit.io). Machine-readable version: /api/dataset/, or get the CSV + JSON on GitHub.
Prefer the terminal? The same dataset and engine power the open-source CLI: npx @wecko-ai/modelfit names the best local model for the machine it runs on. Get it on npm or GitHub.
| Family | Quant | Local | ollama | ||||
|---|---|---|---|---|---|---|---|
| Claude 3.5 Sonnet | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude 3.7 Sonnet | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude 3 Opus | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude 4 Opus | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude 4 Sonnet | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude Opus 4.7 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude Opus 4.8 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude Fable 5 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| DeepSeek-R1 Distill Qwen 7B | DeepSeek | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | deepseek-r1:7b |
| DeepSeek-R1 Distill Qwen 14B | DeepSeek | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | deepseek-r1:14b |
| DeepSeek-R1 Distill Llama 70B | DeepSeek | 70B | Q4_K_M | 64 GB | ~42 GB | Yes | deepseek-r1:70b |
| DeepSeek V4 Flash | DeepSeek | 284B | API | 0 GB | — | Open (llama.cpp) | — |
| DeepSeek-R1 671B | DeepSeek | 671B | Q4_K_M | 512 GB | ~380 GB | Yes | deepseek-r1:671b-q4_K_M |
| DeepSeek-V3 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| DeepSeek-V3-0324 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| DeepSeek-R1 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| DeepSeek V4 Pro | DeepSeek | 1600B | API | 0 GB | — | Open, no fit | — |
| Gemma 3 1B Instruct | Gemma | 1B | Q4_K_M | 2 GB | ~1 GB | Yes | gemma3:1b |
| Gemma 2 2B Instruct | Gemma | 2B | Q4_K_M | 4 GB | ~1.8 GB | Yes | gemma2:2b-instruct-q4_K_M |
| Gemma 4 E2B | Gemma | 2.3B | Q4_K_M | 4 GB | ~2.3 GB | Yes | gemma4:e2b |
| Gemma 3 4B Instruct | Gemma | 4B | Q4_K_M | 6 GB | ~3.5 GB | Yes | gemma3:4b |
| Gemma 4 E4B | Gemma | 4.5B | Q4_K_M | 6 GB | ~4 GB | Yes | gemma4:e4b |
| Gemma 2 9B Instruct | Gemma | 9B | Q4_K_M | 12 GB | ~7 GB | Yes | gemma2:9b-instruct-q4_K_M |
| Gemma 3 12B Instruct | Gemma | 12B | Q4_K_M | 16 GB | ~9.5 GB | Yes | gemma3:12b |
| Gemma 4 12B | Gemma | 12B | Q4_K_M | 12 GB | ~8 GB | Yes | gemma4:12b |
| Gemma 4 12B (Q8) | Gemma | 12B | Q8_0 | 24 GB | ~12.8 GB | Yes | gemma4:12b-it-q8_0 |
| Gemma 4 26B-A4B | Gemma | 26B | Q4_K_M | 24 GB | ~16 GB | Yes | gemma4:26b |
| Gemma 4 26B-A4B (Q8) | Gemma | 26B | Q8_0 | 48 GB | ~28.1 GB | Yes | gemma4:26b-a4b-it-q8_0 |
| Gemma 2 27B Instruct | Gemma | 27B | Q4_K_M | 32 GB | ~21 GB | Yes | gemma2:27b-instruct-q4_K_M |
| Gemma 3 27B Instruct | Gemma | 27B | Q4_K_M | 32 GB | ~21 GB | Yes | gemma3:27b |
| Gemma 4 31B | Gemma | 31B | Q4_K_M | 32 GB | ~20 GB | Yes | gemma4:31b |
| Gemini 2.5 Pro | Undisclosed | API | 0 GB | — | Cloud | — | |
| Gemini 2.5 Flash | Undisclosed | API | 0 GB | — | Cloud | — | |
| Gemini 3.1 Pro | Undisclosed | API | 0 GB | — | Cloud | — | |
| GPT-OSS 20B | GPT-OSS | 21B | MXFP4 | 24 GB | ~13.8 GB | Yes | gpt-oss:20b |
| GPT-OSS 120B | GPT-OSS | 117B | MXFP4 | 96 GB | ~65.4 GB | Yes | gpt-oss:120b |
| Granite 4.1 3B Instruct | Granite | 3B | Q4_K_M | 4 GB | ~2 GB | Yes | granite4.1:3b |
| Granite 4.1 8B Instruct | Granite | 8B | Q4_K_M | 8 GB | ~5.5 GB | Yes | granite4.1:8b |
| Kimi K2 Instruct | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| Kimi K2.6 | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| Kimi K2.7-Code | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| Poolside Laguna XS.2 | Laguna | 33B | Q4_K_M | 36 GB | ~23 GB | Yes | laguna-xs.2:q4_K_M |
| LFM2.5 8B-A1B | LFM2 | 8.3B | Q4_K_M | 8 GB | ~5.5 GB | Yes | lfm2.5:8b-a1b-q4_K_M |
| LFM2 24B-A2B Instruct | LFM2 | 24B | Q4_K_M | 24 GB | ~14 GB | Yes | lfm2:24b-a2b |
| Llama 3.2 1B Instruct | Llama | 1B | Q4_K_M | 2 GB | ~1 GB | Yes | llama3.2:1b-instruct-q4_K_M |
| Llama 3.2 3B Instruct | Llama | 3B | Q4_K_M | 4 GB | ~2.5 GB | Yes | llama3.2:3b-instruct-q4_K_M |
| Llama 3.1 8B Instruct | Llama | 8B | Q4_K_M | 12 GB | ~6.5 GB | Yes | llama3.1:8b-instruct-q4_K_M |
| Llama 3.1 8B Instruct (Q5) | Llama | 8B | Q5_K_M | 12 GB | ~8 GB | Yes | llama3.1:8b-instruct-q5_K_M |
| Llama 3.1 70B Instruct | Llama | 70B | Q4_K_M | 64 GB | ~42 GB | Yes | llama3.1:70b-instruct-q4_K_M |
| Llama 3.3 70B Instruct | Llama | 70B | Q4_K_M | 64 GB | ~42 GB | Yes | llama3.3:70b-instruct-q4_K_M |
| Llama 3.3 70B Instruct (Q8) | Llama | 70B | Q8_0 | 96 GB | ~75 GB | Yes | llama3.3:70b-instruct-q8_0 |
| Llama 3.3 70B Instruct (Q6) | Llama | 70B | Q6_K | 96 GB | ~57.9 GB | Yes | llama3.3:70b-instruct-q6_K |
| Llama 4 Scout | Llama | 109B | Q4_K_M | 96 GB | ~67 GB | Yes | llama4:scout |
| Llama 4 Maverick | Llama | 400B | Q4_K_M | 320 GB | ~245 GB | Yes | llama4:maverick |
| Llama 3.1 405B Instruct | Llama | 405B | Q4_K_M | 320 GB | ~243 GB | Yes | llama3.1:405b-instruct-q4_K_M |
| Xiaomi MiMo-V2-Flash | MiMo | 309B | API | 0 GB | — | Open (llama.cpp) | — |
| MiniMax M3 | MiniMax | 428B | API | 0 GB | — | Open, no fit | — |
| Mistral 7B Instruct | Mistral | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | mistral:7b-instruct-q4_K_M |
| Mistral Nemo 12B | Mistral | 12B | Q4_K_M | 16 GB | ~9.5 GB | Yes | mistral-nemo:12b |
| Mistral Small 22B | Mistral | 22B | Q4_K_M | 32 GB | ~17 GB | Yes | mistral-small:22b |
| Mistral Small 3.1 | Mistral | 24B | Q4_K_M | 24 GB | ~15 GB | Yes | mistral-small3.1:24b |
| Mixtral 8x7B Instruct | Mistral | 46.7B | Q4_K_M | 48 GB | ~30 GB | Yes | mixtral:8x7b |
| Mistral Medium 3.5 | Mistral | 128B | API | 0 GB | — | Open (llama.cpp) | — |
| NVIDIA Nemotron Cascade 2 30B-A3B | Nemotron | 30B | Q6_K | 36 GB | ~24 GB | Yes | nemotron-cascade-2:30b |
| NVIDIA Nemotron 3 Ultra | Nemotron | 550B | API | 0 GB | — | Open, no fit | — |
| Cohere North Mini Code | North | 30B | Q4_K_M | 32 GB | ~19 GB | Yes | north-mini-code-1.0:q4_K_M |
| GPT-4o | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| GPT-4o mini | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| GPT-5.5 | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| Phi-3 Mini 3.8B | Phi | 3.8B | Q4_K_M | 6 GB | ~3.2 GB | Yes | phi3:mini |
| Phi-4 Mini 3.8B | Phi | 3.8B | Q4_K_M | 6 GB | ~3.2 GB | Yes | phi4-mini:3.8b |
| Phi-3 Medium 14B | Phi | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | phi3:medium |
| Phi-4 14B | Phi | 14B | Q4_K_M | 24 GB | ~11.5 GB | Yes | phi4:14b-q4_K_M |
| Qwen3.7-Plus | Qwen | Undisclosed | API | 0 GB | — | Cloud | — |
| Qwen2.5 0.5B Instruct | Qwen | 0.5B | Q4_K_M | 2 GB | ~0.8 GB | Yes | qwen2.5:0.5b-instruct-q4_K_M |
| Qwen3.5 0.8B Instruct | Qwen | 0.8B | Q4_K_M | 2 GB | ~0.8 GB | Yes | qwen3.5:0.8b |
| Qwen2.5 1.5B Instruct | Qwen | 1.5B | Q4_K_M | 4 GB | ~1.5 GB | Yes | qwen2.5:1.5b-instruct-q4_K_M |
| Qwen3.5 2B Instruct | Qwen | 2B | Q4_K_M | 4 GB | ~1.8 GB | Yes | qwen3.5:2b |
| Qwen2.5 3B Instruct | Qwen | 3B | Q4_K_M | 4 GB | ~2.5 GB | Yes | qwen2.5:3b-instruct-q4_K_M |
| Qwen3.5 4B Instruct | Qwen | 4B | Q4_K_M | 6 GB | ~3.5 GB | Yes | qwen3.5:4b |
| Qwen2.5 7B Instruct | Qwen | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | qwen2.5:7b-instruct-q4_K_M |
| Qwen2.5 Coder 7B | Qwen | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | qwen2.5-coder:7b |
| Qwen3 8B | Qwen | 8B | Q4_K_M | 12 GB | ~6.5 GB | Yes | qwen3:8b-q4_K_M |
| Qwen3.5 9B Instruct | Qwen | 9B | Q4_K_M | 12 GB | ~7 GB | Yes | qwen3.5:9b |
| Qwen3.5 9B Instruct (Q8) | Qwen | 9B | Q8_0 | 16 GB | ~10.7 GB | Yes | qwen3.5:9b-q8_0 |
| Qwen2.5 14B Instruct | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen2.5:14b-instruct-q4_K_M |
| Qwen2.5 Coder 14B | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen2.5-coder:14b |
| Qwen3 14B | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen3:14b-q4_K_M |
| Qwen3 14B (Q8) | Qwen | 14B | Q8_0 | 24 GB | ~15.9 GB | Yes | qwen3:14b-q8_0 |
| Qwen3.5 27B Instruct | Qwen | 27B | Q4_K_M | 24 GB | ~16 GB | Yes | qwen3.5:27b |
| Qwen3.6 27B | Qwen | 27B | Q4_K_M | 32 GB | ~18 GB | Yes | qwen3.6:27b |
| Qwen3.6 27B (Q8) | Qwen | 27B | Q8_0 | 48 GB | ~30 GB | Yes | qwen3.6:27b-q8_0 |
| Qwen3 30B | Qwen | 30B | Q4_K_M | 32 GB | ~22 GB | Yes | qwen3:30b |
| Qwen3.5 35B-A3B Instruct | Qwen | 35B | Q4_K_M | 32 GB | ~20 GB | Yes | qwen3.5:35b-a3b |
| Qwen3.6 35B-A3B | Qwen | 35B | Q4_K_M | 32 GB | ~22 GB | Yes | qwen3.6:35b-a3b |
| Qwen3.6 35B-A3B (Q8) | Qwen | 35B | Q8_0 | 64 GB | ~38.7 GB | Yes | qwen3.6:35b-a3b-q8_0 |
| Qwen3.5 35B-A3B Instruct (Q8) | Qwen | 35B | Q8_0 | 64 GB | ~38.7 GB | Yes | qwen3.5:35b-a3b-q8_0 |
| Qwen3-Next 80B-A3B | Qwen | 80B | Q4_K_M | 72 GB | ~50.4 GB | Yes | qwen3-next:80b |
| Qwen3-Next 80B-A3B (Q8) | Qwen | 80B | Q8_0 | 128 GB | ~84.8 GB | Yes | qwen3-next:80b-a3b-instruct-q8_0 |
| Qwen3.5 122B-A10B Instruct | Qwen | 122B | Q4_K_M | 96 GB | ~72 GB | Yes | qwen3.5:122b-a10b |
| Qwen3 235B A22B | Qwen | 235B | Q4_K_M | 192 GB | ~130 GB | Yes | qwen3:235b-a22b-q4_K_M |
| SmolLM2 360M | SmolLM | 0.36B | Q4_K_M | 1 GB | ~0.5 GB | Yes | smollm2:360m |
| Grok 4.3 | xAI | Undisclosed | API | 0 GB | — | Cloud | — |
| GLM-4 Plus | Zhipu | Undisclosed | API | 0 GB | — | Cloud | — |
| GLM-5.2 | Zhipu | Undisclosed | API | 0 GB | — | Cloud | — |
| GLM-5 | Zhipu | 744B | API | 0 GB | — | Open, no fit | — |
| GLM-5.1 | Zhipu | 744B | API | 0 GB | — | Open, no fit | — |
Estimated load = approximate memory at Q4_K_M; estimates, not measured. All local entries are GGUF builds pulled via Ollama, so they also run in llama.cpp and LM Studio. See the hardware stats for RAM-tier guidance.
Frequently asked questions
What does the ModelFit compatibility matrix show?
Every model ModelFit tracks (107 total, 75 local), with its parameter size, quantization, minimum RAM, and estimated memory load, so you can see at a glance which local AI models run on which Apple Silicon or NVIDIA hardware.
Can I download the dataset?
Yes. A machine-readable JSON export is free at /api/dataset/, licensed CC BY 4.0. The same data is also mirrored on GitHub and Hugging Face for offline use or bulk analysis.
Does "runs locally: false" mean a model is closed?
No. runsLocally tracks whether a registry-verified Ollama build fits a consumer RAM tier ModelFit maps (up to 256GB). Some open-weight models publish weights but exceed every tier. NVIDIA Nemotron 3 Ultra (550B, OpenMDW license) needs roughly 190 GB even at 2-bit quantization, so it is marked openWeights: true with runsLocally: false.
How is model fit calculated?
A model needs roughly 0.6 GB of memory per billion parameters at Q4_K_M quantization. ModelFit sizes recommendations to ~70% of a device's unified memory up to 32GB, scaling to ~85% at 128GB and above, leaving headroom for the OS, context, and KV-cache. On high-RAM Macs you can raise the GPU-wired ceiling further with iogpu.wired_limit_mb.