Guides · Updated 2026-10-17
Best local AI models for a 24GB GPU (2026)
RTX 3090, 4090 or RX 7900 XTX? Here are the best 27-35B Ollama models that run fully on 24GB, from Qwen3.6 to Gemma 4 31B.
Not sure which model to pull? Use the GPU model picker: enter your VRAM and RAM and get a list of Ollama models that fit.
A 24 GB card runs the best models most people can host at home: 27-32B dense models and 26-35B mixture-of-experts models, fully on the GPU.
Top picks
| Use | Model | Command | Download |
|---|---|---|---|
| Best all-rounder | Qwen3.6 27B | ollama run qwen3.6:27b | 17 GB |
| Best local coder | Qwen3.6 27B coding | ollama run qwen3.6:27b-coding | 18 GB |
| Google flagship | Gemma 4 31B | ollama run gemma4:31b | 20 GB |
| Fast MoE | Gemma 4 26B-A4B | ollama run gemma4:26b | 18 GB |
| Agentic coding MoE | Qwen3-Coder 30B-A3B | ollama run qwen3-coder:30b | 19 GB |
| Reasoning | DeepSeek-R1 32B distill | ollama run deepseek-r1:32b | 20 GB |
| Writing | Gemma 3 27B | ollama run gemma3:27b | 17 GB |
Tips
- 17-18 GB models leave room for 16-32K context; 20 GB models are best kept to shorter contexts.
qwen3.6:35b(24 GB download) doesn’t fully fit on a 24 GB card, but because only ~3B parameters are active it still runs well split with system RAM.- 70B models (
llama3.3:70b, 43 GB) need 48 GB+: two 24 GB cards, a 128 GB unified-memory mini PC, or a big Mac. - Used RTX 3090s remain the value king: 936 GB/s bandwidth, nearly matching a 4090 for generation speed.
Check your setup in the GPU picker, or see 24GB hardware picks.
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