Local AI Model Browser
Filter every open-weight model you can run on your own machine by family, tier, VRAM budget, license and task. Requirements shown are for the Q4 quantized build — the best balance of quality and size. Every model runs free with Ollama.
Or browse curated collections (by VRAM, family, license, task) →16 models
of 16 local modelsLlama 3.1 8B
Meta · Llama 3.1 Community
Meta's 8B workhorse with a massive 128K context window. Punches well above its weight class for everyday coding and chat tasks, runs comfortably on any consumer GPU with 6 GB VRAM, and delivers near-70B quality on many benchmarks.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run llama3.1:8bLlama 3.3 70B
Meta · Llama 3.3 Community
Meta's flagship open-weight model and the go-to choice for users who want GPT-4-class performance without sending data to the cloud. Consistently tops open-source leaderboards. Needs ~48 GB VRAM at Q4_K_M — a dual-GPU rig or Apple Silicon with 48 GB unified memory.
Min VRAM
48 GB
Context
128K
Run with Ollama
ollama run llama3.3:70bLlama 3.2 3B
Meta · Llama 3.2 Community
The smallest production-quality Llama model, designed to run on laptops, Raspberry Pis, and other edge devices with minimal VRAM. Response latency is sub-second on modern CPUs, making it ideal for real-time assistants and local automation scripts.
Min VRAM
2 GB
Context
128K
Run with Ollama
ollama run llama3.2:3bQwen3 8B
Alibaba · Apache 2.0
One of the most-downloaded mid-size AI models on HuggingFace in 2026, with tens of millions of downloads. Qwen3 8B outperforms the previous Qwen2.5 14B on most benchmarks, supports hybrid thinking mode and tool calling, and runs on any GPU with 6 GB VRAM. Apache 2.0 license — fully commercial.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run qwen3:8bMistral 7B v0.3
Mistral AI · Apache 2.0
The model that proved 7B parameters can match much larger models on reasoning tasks when trained carefully. Apache 2.0 licensed, so it is fully free to use commercially with no restrictions.
Min VRAM
4 GB
Context
32K
Run with Ollama
ollama run mistral:7bMixtral 8x7B
Mistral AI · Apache 2.0
A sparse mixture-of-experts model that activates only 12B parameters per token while carrying 47B total, delivering 70B-class results at a fraction of the compute cost. One of the best open-weight models available under a fully permissive license.
Min VRAM
26 GB
Context
32K
Run with Ollama
ollama run mixtral:8x7bMistral Nemo 12B
Mistral AI · Apache 2.0
Built in partnership with NVIDIA and trained on a broad multilingual corpus, Nemo is Mistral's best model for non-English tasks across European and Asian languages. The 128K context window makes it practical for processing long documents locally.
Min VRAM
7 GB
Context
128K
Run with Ollama
ollama run mistral-nemo:12bGemma 4 12B
Google · Apache 2.0
Google's latest Gemma generation doubles the context window to 256K tokens and — a major shift from earlier Gemma releases — ships under the Apache 2.0 license. The 12B model is one of the most actively fine-tuned coding bases on HuggingFace in 2026, with hundreds of community GGUF variants. Runs on any GPU with 10 GB VRAM.
Min VRAM
8 GB
Context
256K
Run with Ollama
ollama run gemma4:12bGemma 3 4B
Google · Gemma Terms of Use
Google's compact Gemma 3 model brings the quality of a much larger system into a 4B package that runs on integrated graphics. A strong first choice for anyone wanting a capable, low-power model without needing a discrete GPU.
Min VRAM
3 GB
Context
128K
Run with Ollama
ollama run gemma3:4bGemma 3 27B
Google · Gemma Terms of Use
The largest Gemma 3 model and one of the best open-weight models in the 20–30B range. Fits in a single RTX 4090 (24 GB) with room to spare at Q4 quantization, and delivers instruction-following quality that rivals models twice its size.
Min VRAM
15 GB
Context
128K
Run with Ollama
ollama run gemma3:27bPhi-4 14B
Microsoft · MIT
Microsoft's research-driven Phi-4 focuses on synthetic high-quality training data, producing a 14B model that outperforms much larger models on coding and mathematical reasoning benchmarks. MIT licensed with zero usage restrictions for commercial projects.
Min VRAM
8 GB
Context
16K
Run with Ollama
ollama run phi4:14bDeepSeek R1 Distill 8B
DeepSeek · MIT
A distilled version of DeepSeek's R1 reasoning model that inherits chain-of-thought problem-solving capabilities in a compact 8B package. One of the few small models that can reliably work through multi-step math and logic problems without external tooling.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run deepseek-r1:8bDeepSeek V4 Flash
DeepSeek · MIT
The faster, more practical sibling of the 1.6-trillion-parameter DeepSeek V4 Pro. At 284B total it activates only ~13B parameters per token, so once loaded it generates far faster than its size suggests — but even at 4-bit it needs roughly 180 GB of memory, putting it in 256 GB+ unified-memory Mac or multi-GPU server territory. MIT licensed, with a 1M-token context window.
Min VRAM
~180 GB (Q4)
Context
1M
Run with Ollama
ollama run deepseek-v4-flashOpenAI gpt-oss-20b
OpenAI · Apache 2.0
OpenAI's first commercially-licensed open-weight model — a historic release under Apache 2.0. MoE architecture keeps only 3.6B parameters active at inference, so it generates tokens at nearly 7B speed despite 20B total params. Supports function calling and web browsing. Over 10M downloads across Ollama and HuggingFace. Pull it with ollama run gpt-oss:20b.
Min VRAM
12 GB
Context
128K
Run with Ollama
ollama run gpt-oss:20bOrnith 1.0 35B
DeepReinforce · MIT
Scores 75.6% on SWE-bench Verified — one of the highest results for any open-weight coding model and above most paid assistants. From the lab DeepReinforce, trained with RL self-improvement for terminal control and tool calling. MIT licensed, 262K context. There is no Ollama library tag yet — pull the GGUF directly with ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF.
Min VRAM
20 GB
Context
262K
Run with Ollama
ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUFQwen 2.5 Coder 7B
Alibaba · Apache 2.0
A coding-specialist model fine-tuned on a massive corpus of source code across 40+ programming languages, delivering autocomplete and generation quality that rivals dedicated IDE tools. At 7B it is fast enough for real-time code suggestions on a single consumer GPU.
Min VRAM
4 GB
Context
128K
Run with Ollama
ollama run qwen2.5-coder:7b