AI Model Families for Local Inference
Browse open-weight model families you can run locally with Ollama. Each family page shows all variants, RAM requirements, device compatibility, and performance expectations.
Catalog coverage
Quality-gated, not inflated
ModelFit tracks 141 models across 24 families, with 106 local rows. Every Ollama command is registry-verified before it ships, and low-quality 2-bit quants stay out of the ranking even when they technically exist.
141 tracked106 local7 quant classes105 registry-verified
Default policy
Q4_K_M is the default recommendation tier; Q6/Q8 variants appear when they add real quality headroom.
Freshness
Catalog snapshot updated 2026-09-03; new tags enter through the registry-check pipeline, not from scraped guesses.
Honest coverage
A smaller verified catalog beats a bigger list padded with broken tags and unusable quants.
Local-Runnable Models
AI Model Families for Local Use
Alibaba CloudQwen
Qwen is Alibaba Cloud's open-weight model family with the widest range of sizes, from 0.5B to 235B parameters. Known for strong multilingual performance and coding ability.
34 models·0.5B–235B
Widest size range (0.5B to 235B)Strong multilingual and coding performance
MetaLlama
Llama is Meta's open-weight model family and the most popular choice for local AI. Known for strong general reasoning and a massive community ecosystem.
12 models·1B–405B
Most popular open-weight model familyStrong general reasoning and instruction following
DeepSeek AIDeepSeek
DeepSeek specializes in reasoning and coding models. DeepSeek R1 introduced chain-of-thought reasoning that rivals proprietary models, while V3 is a massive MoE model.
6 models·7B–671B
Best-in-class reasoning with R1 modelsStrong coding performance
Mistral AIMistral
Mistral AI's models are known for efficiency and strong performance relative to their size. Mistral 7B was a breakthrough that proved small models could compete with much larger ones.
9 models·7B–46.7B
Excellent performance-per-parameter ratioSliding window attention for efficiency
Google DeepMindGemma
Gemma is Google DeepMind's lightweight open model family. Known for excellent quality at small sizes and strong safety tuning.
19 models·1B–31B
Excellent quality at small sizes (1B-9B)Strong safety and instruction tuning
MicrosoftPhi
Phi is Microsoft's small-but-mighty model family, built on the idea that careful training data beats raw parameter count. Phi-4 Mini packs strong reasoning into just 3.8B parameters, while Phi-4 14B competes with much larger models on quality. Both run locally with Ollama and pair naturally with low-RAM Apple Silicon Macs.
6 models·3.8B–14B
Best quality-per-gigabyte at small sizesPhi-4 Mini 3.8B loads in just 3.2GB (7GB min RAM)
Liquid AILFM2
LFM2 is Liquid AI's efficiency-focused model family, built on a hybrid architecture rather than a standard dense transformer. Its flagship, LFM2 24B-A2B, is a sparse mixture-of-experts model that activates only 2B of its 24B parameters per token. That design makes it fast on consumer hardware and well suited to agent workflows, tool calling, and privacy-sensitive local setups.
2 models·8.3B–24B
Hybrid MoE design: 24B total parameters, only 2B active per tokenLoads in about 14GB, fits any 16GB Mac
Hugging FaceSmolLM
SmolLM is Hugging Face's ultra-tiny model family for the most constrained devices. SmolLM2 360M loads in about 0.5GB and runs on anything with 1GB of RAM, from old Macs and iPhones to embedded boards. It is the smallest model in our database and the fastest by speed score.
1 models·0.36B–0.36B
Tiny 360M-parameter model from Hugging FaceLoads in about 0.5GB, needs just 1GB of RAM
Full Model Catalog
Filter every tracked model by RAM, family, runtime and context budget. Load figures and fit rules come from the same dataset that powers the wizard.
141 of 141 models
| Family | Quant | Local | ollama | ||||
|---|---|---|---|---|---|---|---|
| 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 |
| Qwen2.5 3B Instruct | Qwen | 3B | Q4_K_M | 4 GB | ~2.5 GB | Yes | qwen2.5:3b-instruct-q4_K_M |
| Qwen2.5 7B Instruct | Qwen | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | qwen2.5:7b-instruct-q4_K_M |
| Qwen2.5 14B Instruct | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen2.5:14b-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 (Q8) | Llama | 8B | Q8_0 | 16 GB | ~8 GB | Yes | llama3.1:8b-instruct-q8_0 |
| 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 |
| Mistral 7B Instruct | Mistral | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | mistral:7b-instruct-q4_K_M |
| Mistral 7B Instruct (Q5) | Mistral | 7B | Q5_K_M | 8 GB | ~4.8 GB | Yes | mistral:7b-instruct-q5_K_M |
| Mistral 7B Instruct (Q8) | Mistral | 7B | Q8_0 | 16 GB | ~7.2 GB | Yes | mistral:7b-instruct-q8_0 |
| Mixtral 8x7B Instruct | Mistral | 46.7B | Q4_K_M | 48 GB | ~30 GB | Yes | mixtral:8x7b |
| Mistral Nemo 12B | Mistral | 12B | Q4_K_M | 16 GB | ~9.5 GB | Yes | mistral-nemo:12b |
| Mistral Nemo 12B (Q8) | Mistral | 12B | Q8_0 | 24 GB | ~12.1 GB | Yes | mistral-nemo:12b-instruct-2407-q8_0 |
| Gemma 2 2B Instruct | Gemma | 2B | Q4_K_M | 4 GB | ~1.8 GB | Yes | gemma2:2b-instruct-q4_K_M |
| Gemma 2 9B Instruct | Gemma | 9B | Q4_K_M | 12 GB | ~7 GB | Yes | gemma2:9b-instruct-q4_K_M |
| Gemma 2 27B Instruct | Gemma | 27B | Q4_K_M | 32 GB | ~21 GB | Yes | gemma2:27b-instruct-q4_K_M |
| Phi-3 Mini 3.8B | Phi | 3.8B | Q4_K_M | 6 GB | ~3.2 GB | Yes | phi3:mini |
| 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 |
| Phi-4 14B (Q8) | Phi | 14B | Q8_0 | 24 GB | ~14.5 GB | Yes | phi4:14b-q8_0 |
| Qwen2.5 Coder 7B | Qwen | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | qwen2.5-coder:7b |
| Qwen2.5 Coder 14B | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen2.5-coder:14b |
| Llama 3.2 1B Instruct | Llama | 1B | Q4_K_M | 2 GB | ~1 GB | Yes | llama3.2:1b-instruct-q4_K_M |
| Mistral Small 22B | Mistral | 22B | Q4_K_M | 32 GB | ~17 GB | Yes | mistral-small:22b |
| Kimi K2 Instruct | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| 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 | — |
| 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 |
| Gemma 3 1B Instruct | Gemma | 1B | Q4_K_M | 2 GB | ~1 GB | Yes | gemma3:1b |
| Gemma 3 1B Instruct (Q8) | Gemma | 1B | Q8_0 | 4 GB | ~1 GB | Yes | gemma3:1b-it-q8_0 |
| Phi-4 Mini 3.8B | Phi | 3.8B | Q4_K_M | 6 GB | ~3.2 GB | Yes | phi4-mini:3.8b |
| Phi-4 Mini 3.8B (Q8) | Phi | 3.8B | Q8_0 | 8 GB | ~3.8 GB | Yes | phi4-mini:3.8b-q8_0 |
| SmolLM2 360M | SmolLM | 0.36B | Q4_K_M | 1 GB | ~0.5 GB | Yes | smollm2:360m |
| Llama 3.3 70B Instruct | Llama | 70B | Q4_K_M | 64 GB | ~42 GB | Yes | llama3.3:70b-instruct-q4_K_M |
| Llama 3.1 405B Instruct | Llama | 405B | Q4_K_M | 320 GB | ~243 GB | Yes | llama3.1:405b-instruct-q4_K_M |
| DeepSeek-R1 671B | DeepSeek | 671B | Q4_K_M | 512 GB | ~380 GB | Yes | deepseek-r1:671b-q4_K_M |
| Qwen3 8B | Qwen | 8B | Q4_K_M | 12 GB | ~6.5 GB | Yes | qwen3:8b-q4_K_M |
| Qwen3 8B (Q8) | Qwen | 8B | Q8_0 | 16 GB | ~8.1 GB | Yes | qwen3:8b-q8_0 |
| Qwen3 14B | Qwen | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | qwen3:14b-q4_K_M |
| Qwen3 30B | Qwen | 30B | Q4_K_M | 32 GB | ~22 GB | Yes | qwen3:30b |
| Qwen3 30B (Q8) | Qwen | 30B | Q8_0 | 48 GB | ~30.3 GB | Yes | qwen3:30b-a3b-q8_0 |
| Gemma 3 4B Instruct | Gemma | 4B | Q4_K_M | 6 GB | ~3.5 GB | Yes | gemma3:4b |
| Gemma 3 4B Instruct (Q8) | Gemma | 4B | Q8_0 | 8 GB | ~3.9 GB | Yes | gemma3:4b-it-q8_0 |
| Gemma 3 12B Instruct | Gemma | 12B | Q4_K_M | 16 GB | ~9.5 GB | Yes | gemma3:12b |
| Gemma 3 27B Instruct | Gemma | 27B | Q4_K_M | 32 GB | ~21 GB | Yes | gemma3:27b |
| DeepSeek-R1 Distill Qwen 7B | DeepSeek | 7B | Q4_K_M | 8 GB | ~5.5 GB | Yes | deepseek-r1:7b |
| DeepSeek-R1 Distill Qwen 7B (Q8) | DeepSeek | 7B | Q8_0 | 16 GB | ~7.5 GB | Yes | deepseek-r1:7b-qwen-distill-q8_0 |
| DeepSeek-R1 Distill Qwen 14B | DeepSeek | 14B | Q4_K_M | 16 GB | ~11 GB | Yes | deepseek-r1:14b |
| DeepSeek-R1 Distill Qwen 14B (Q8) | DeepSeek | 14B | Q8_0 | 24 GB | ~14.6 GB | Yes | deepseek-r1:14b-qwen-distill-q8_0 |
| DeepSeek-R1 Distill Llama 70B | DeepSeek | 70B | Q4_K_M | 64 GB | ~42 GB | Yes | deepseek-r1:70b |
| GPT-4o | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| GPT-4o mini | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude 4 Sonnet | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Gemini 2.5 Pro | Undisclosed | API | 0 GB | — | Cloud | — | |
| Gemini 2.5 Flash | Undisclosed | API | 0 GB | — | Cloud | — | |
| DeepSeek-V3 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| GLM-5 | Zhipu | 744B | API | 0 GB | — | Open, no fit | — |
| GLM-4 Plus | Zhipu | Undisclosed | API | 0 GB | — | Cloud | — |
| Qwen3 235B A22B | Qwen | 235B | Q4_K_M | 192 GB | ~130 GB | Yes | qwen3:235b-a22b-q4_K_M |
| Mistral Small 3.1 | Mistral | 24B | Q4_K_M | 24 GB | ~15 GB | Yes | mistral-small3.1:24b |
| Mistral Small 3.1 (Q8) | Mistral | 24B | Q8_0 | 36 GB | ~23.3 GB | Yes | mistral-small3.1:24b-instruct-2503-q8_0 |
| DeepSeek-V3-0324 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| DeepSeek-R1 | DeepSeek | 671B | API | 0 GB | — | Open, no fit | — |
| Qwen3.5 0.8B Instruct | Qwen | 0.8B | Q4_K_M | 2 GB | ~0.8 GB | Yes | qwen3.5:0.8b |
| Qwen3.5 2B Instruct | Qwen | 2B | Q4_K_M | 4 GB | ~1.8 GB | Yes | qwen3.5:2b |
| Qwen3.5 4B Instruct | Qwen | 4B | Q4_K_M | 6 GB | ~3.5 GB | Yes | qwen3.5:4b |
| Qwen3.5 4B Instruct (Q8) | Qwen | 4B | Q8_0 | 8 GB | ~4.3 GB | Yes | qwen3.5:4b-q8_0 |
| Qwen3.5 9B Instruct | Qwen | 9B | Q4_K_M | 12 GB | ~7 GB | Yes | qwen3.5:9b |
| Qwen3.5 35B-A3B Instruct | Qwen | 35B | Q4_K_M | 32 GB | ~20 GB | Yes | qwen3.5:35b-a3b |
| Qwen3.5 27B Instruct | Qwen | 27B | Q4_K_M | 24 GB | ~16 GB | Yes | qwen3.5:27b |
| Qwen3.5 27B Instruct (Q8) | Qwen | 27B | Q8_0 | 48 GB | ~27.1 GB | Yes | qwen3.5:27b-q8_0 |
| Qwen3.5 122B-A10B Instruct | Qwen | 122B | Q4_K_M | 96 GB | ~72 GB | Yes | qwen3.5:122b-a10b |
| LFM2 24B-A2B Instruct | LFM2 | 24B | Q4_K_M | 24 GB | ~14 GB | Yes | lfm2:24b-a2b |
| LFM2.5 8B-A1B | LFM2 | 8.3B | Q4_K_M | 8 GB | ~5.5 GB | Yes | lfm2.5:8b-a1b-q4_K_M |
| 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 |
| Qwen3.6 27B | Qwen | 27B | Q4_K_M | 32 GB | ~18 GB | Yes | qwen3.6:27b |
| Qwen3.8 27B | Qwen | 27B | Q4_K_M | 24 GB | ~16.5 GB | Yes | qwen3.8:27b |
| Qwen3.8 27B (Q8) | Qwen | 27B | Q8_0 | 48 GB | ~27.1 GB | Yes | qwen3.8:27b-q8_0 |
| Qwen3.6 35B-A3B | Qwen | 35B | Q4_K_M | 32 GB | ~22 GB | Yes | qwen3.6:35b-a3b |
| Gemma 4 31B | Gemma | 31B | Q4_K_M | 32 GB | ~20 GB | Yes | gemma4:31b |
| Gemma 4 31B (Q8) | Gemma | 31B | Q8_0 | 48 GB | ~30.9 GB | Yes | gemma4:31b-it-q8_0 |
| Gemma 4 26B-A4B | Gemma | 26B | Q4_K_M | 24 GB | ~16 GB | Yes | gemma4:26b |
| Gemma 4 E4B | Gemma | 4.5B | Q4_K_M | 6 GB | ~4 GB | Yes | gemma4:e4b |
| Gemma 4 E4B (Q8) | Gemma | 4.5B | Q8_0 | 16 GB | ~7.5 GB | Yes | gemma4:e4b-it-q8_0 |
| Gemma 4 E2B | Gemma | 2.3B | Q4_K_M | 4 GB | ~2.3 GB | Yes | gemma4:e2b |
| Gemma 4 E2B (Q8) | Gemma | 2.3B | Q8_0 | 8 GB | ~4.6 GB | Yes | gemma4:e2b-it-q8_0 |
| 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 |
| Mistral Medium 3.5 | Mistral | 128B | API | 0 GB | — | Open (llama.cpp) | — |
| DeepSeek V4 Flash 0731 | DeepSeek | 284B | API | 0 GB | — | Open (llama.cpp) | — |
| DeepSeek V4 Pro | DeepSeek | 1600B | API | 0 GB | — | Open, no fit | — |
| Kimi K2.6 | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| GLM-5.1 | Zhipu | 744B | API | 0 GB | — | Open, no fit | — |
| Claude Opus 4.7 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| Claude Opus 4.8 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| GPT-5.5 | OpenAI | Undisclosed | API | 0 GB | — | Cloud | — |
| Gemini 3.1 Pro | Undisclosed | API | 0 GB | — | Cloud | — | |
| Grok 4.3 | xAI | Undisclosed | API | 0 GB | — | Cloud | — |
| Gemma 4 12B | Gemma | 12B | Q4_K_M | 12 GB | ~8 GB | Yes | gemma4:12b |
| Claude Fable 5 | Claude | Undisclosed | API | 0 GB | — | Cloud | — |
| GLM-5.2 | Zhipu | 753B | API | 0 GB | — | Open, no fit | — |
| Kimi K2.7-Code | Kimi | 1000B | API | 0 GB | — | Open, no fit | — |
| MiniMax M3 | MiniMax | 428B | API | 0 GB | — | Open, no fit | — |
| Qwen3.7-Plus | Qwen | Undisclosed | API | 0 GB | — | Cloud | — |
| NVIDIA Nemotron 3 Ultra | Nemotron | 550B | API | 0 GB | — | Open, no fit | — |
| Xiaomi MiMo-V2-Flash | MiMo | 309B | 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 |
| Poolside Laguna XS.2 | Laguna | 33B | Q4_K_M | 36 GB | ~23 GB | Yes | laguna-xs.2:q4_K_M |
| Cohere North Mini Code | North | 30B | Q4_K_M | 32 GB | ~19 GB | Yes | north-mini-code-1.0:q4_K_M |
| GPT-OSS 120B | GPT-OSS | 117B | MXFP4 | 96 GB | ~65.4 GB | Yes | gpt-oss:120b |
| GPT-OSS 20B | GPT-OSS | 21B | MXFP4 | 24 GB | ~13.8 GB | Yes | gpt-oss:20b |
| Qwen3-Next 80B-A3B | Qwen | 80B | Q4_K_M | 72 GB | ~50.4 GB | Yes | qwen3-next:80b |
| 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 |
| Qwen3.5 9B Instruct (Q8) | Qwen | 9B | Q8_0 | 16 GB | ~10.7 GB | Yes | qwen3.5:9b-q8_0 |
| Qwen3.6 27B (Q8) | Qwen | 27B | Q8_0 | 48 GB | ~30 GB | Yes | qwen3.6:27b-q8_0 |
| Gemma 4 12B (Q8) | Gemma | 12B | Q8_0 | 24 GB | ~12.8 GB | Yes | gemma4:12b-it-q8_0 |
| Gemma 4 26B-A4B (Q8) | Gemma | 26B | Q8_0 | 48 GB | ~28.1 GB | Yes | gemma4:26b-a4b-it-q8_0 |
| Qwen3-Next 80B-A3B (Q8) | Qwen | 80B | Q8_0 | 128 GB | ~84.8 GB | Yes | qwen3-next:80b-a3b-instruct-q8_0 |
| Qwen3 14B (Q8) | Qwen | 14B | Q8_0 | 24 GB | ~15.9 GB | Yes | qwen3:14b-q8_0 |
| 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 |
| Kimi K3 | Kimi | 2800B | API | 0 GB | — | Open, no fit | — |
| Laguna XS 2.1 | Laguna | 33B | Q4_K_M | 32 GB | ~20.3 GB | Yes | laguna-xs-2.1:q4_K_M |
| Laguna S 2.1 | Laguna | 118B | Q4_K_M | 128 GB | ~96 GB | Yes | laguna-s-2.1:q4_K_M |
| Ornith 1.0 9B | Ornith | 9B | Q4_K_M | 8 GB | ~5.6 GB | Yes | ornith:9b |
| Ornith 1.0 35B | Ornith | 35B | Q4_K_M | 32 GB | ~21.2 GB | Yes | ornith:35b |
| Qwen3.8-Flash-Next | Qwen | 125B | IQ1_S | 192 GB | ~123 GB | Yes | — |
| Granite 4.2 3B | Granite | 3.7B | Q4_K_M | 4 GB | ~2.1 GB | Yes | granite4.2:3b |
| Granite 4.2 8B | Granite | 8.8B | Q4_K_M | 8 GB | ~5 GB | Yes | granite4.2:8b |
| Granite 4.2 30B | Granite | 29.3B | Q4_K_M | 24 GB | ~16.5 GB | Yes | granite4.2:30b |
| Nemotron 3.5 Lightning 30B-A3B | Nemotron | 30B | Q4_K_M | 36 GB | ~23.7 GB | Yes | nemotron-3.5-lightning:30b |
| Muse Glimmer 30B | Muse | 29.8B | Q4_K_M | 24 GB | ~16.9 GB | Yes | muse-glimmer:30b |
| MiniCPM-V 4.5 8B | MiniCPM | 8.7B | Q4_K_M | 8 GB | ~5.7 GB | Yes | minicpm-v4.5:8b |
| GLM-5.3 | Zhipu | 753B | API | 0 GB | — | Open, no fit | — |
| GLM-5.3 Flash | Zhipu | 321B | API | 0 GB | — | Open (llama.cpp) | — |
How Much RAM Do You Need?
Largest dense Q4 model that fits each memory tier, from ModelFit’s own catalog. Full breakdown on the stats page.