Local AI Models

Balanced 27B model with strong reasoning. Runs on 16GB VRAM with Q4 quantization.

ollama pull qwen3.5:27b

Ultra-compact 3B model for edge devices and low VRAM setups. Runs on 4GB VRAM.

ollama pull qwen3.5:3b

Alibaba's flagship 72B model with exceptional multilingual capabilities and strong reasoning. Requires multi-GPU or high VRAM setup.

ollama pull qwen3.5:72b

Highly capable 9B model, excellent for consumer hardware. Punches well above its weight class in reasoning tasks.

ollama pull qwen3.5:9b

DeepSeek's 7B reasoning-focused distilled model. Strong chain-of-thought reasoning, runs on 8GB VRAM.

ollama pull deepseek-r1:7b

Google's 27B MoE model with only 4B active parameters per token. Near-frontier quality at a fraction of compute cost.

ollama pull gemma4:27b

Google's flagship dense 31B model with 256K context. Near-frontier quality, top open-source performer on code and reasoning. Arena Elo ~1452.

ollama pull gemma4:31b

Google's ultra-compact multimodal MoE. Only 2.3B active params with full text/image/audio support. Lowest VRAM entry point in the Gemma 4 family.

ollama pull gemma4:e2b

Google's efficient 4B-active MoE model. Excellent performance per compute unit, runs on modest consumer hardware.

ollama pull gemma4:e4b

Meta's efficient MoE model with an unprecedented 10M token context window. 17B active parameters from a 109B total pool.

ollama pull llama4:scout

Microsoft's 14B model with exceptional reasoning for its size. Particularly strong on math, science, and STEM tasks.

ollama pull phi4

MiniMax's self-evolving MoE model with 1M token context. Recently open-sourced under Apache 2.0 license.

ollama pull minimax-m2.7

Mistral's efficient 22B model with strong instruction following and multilingual support. Apache 2.0 license.

ollama pull mistral-small

#1 open-source model on Code Arena. Exceptional for software development, within 3 points of Claude Opus on agentic benchmarks.

ollama pull glm-5.1