Guides · Updated 2026-10-17
Best local AI models for an 8GB GPU (2026)
The Ollama models that fit fully on an 8GB card like the RTX 4060, RTX 5060 or RX 7600, by use case, with exact tags.
With 8 GB of VRAM you can run today’s strong 4B-9B models entirely on the GPU. Keep the model plus context under about 7.5 GB and you’ll get fast, smooth replies.
Top picks
| Use | Model | Command | Download |
|---|---|---|---|
| All-rounder | Qwen3.5 9B | ollama run qwen3.5:9b | 6.6 GB |
| All-rounder (Google) | Gemma 4 E4B | ollama run gemma4:e4b | 6.6 GB |
| Reasoning | DeepSeek-R1 0528 (8B) | ollama run deepseek-r1:8b | 5.2 GB |
| Reasoning, alt | Qwen3 8B | ollama run qwen3:8b | 5.2 GB |
| Coding | Qwen2.5-Coder 7B | ollama run qwen2.5-coder:7b | 4.7 GB |
| Vision | Qwen3-VL 8B | ollama run qwen3-vl:8b | 6.1 GB |
| Long docs | Ministral 3 8B | ollama run ministral-3:8b | 6.0 GB |
Sizes are Ollama download sizes for the default 4-bit tag (checked October 2026).
Tips for 8GB cards
- Keep context modest. 4K-8K tokens is fine; 32K+ will push a 6.6 GB model off the GPU.
- Close VRAM hogs. Browsers, games and video editors all use VRAM. Check
ollama psshows100% GPU. - Want 12-14B models? Gemma 4 12B (8.0 GB) won’t fully fit on 8GB with context. It will run split with RAM, but slower. If that’s your goal, a 12GB or 16GB card is the better buy.
- MoE trick: with 32 GB+ of system RAM,
qwen3:30borqwen3-coder:30b(only 3B active parameters) can run split across GPU and RAM at usable speeds.
Check your exact setup in the GPU picker, or see 8GB hardware picks.
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kits.lilaforge.com → VibeEngineBest local LLM tools, ranked
Ollama, LM Studio, Open WebUI and llama.cpp scored from public evidence, with top picks.
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