Guides · Updated 2026-10-17
Best local AI models for a 16GB GPU (2026)
16GB cards like the RTX 5060 Ti 16GB and RX 9070 XT unlock 20-24B models: gpt-oss 20B, Mistral Small 3.2, Devstral Small 2.
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.
16 GB is where local AI gets genuinely useful for work. You can run 20-24B models fully on the GPU, including OpenAI’s open-weight gpt-oss 20B, which was designed to run in about 16 GB.
Top picks
| Use | Model | Command | Download |
|---|---|---|---|
| All-rounder + reasoning | gpt-oss 20B | ollama run gpt-oss:20b | 14 GB |
| All-rounder + vision | Mistral Small 3.2 24B | ollama run mistral-small3.2 | 15 GB |
| Agentic coding | Devstral Small 2 24B | ollama run devstral-small-2 | 15 GB |
| Fast, long context | Gemma 4 12B | ollama run gemma4:12b | 8.0 GB |
| Reasoning | Phi-4-reasoning 14B | ollama run phi4-reasoning | 11 GB |
| Reasoning (Mistral) | Magistral Small 24B | ollama run magistral | 14 GB |
Tips
- 14-15 GB models are tight. They fit, but keep context short (4-8K). For long context, use Gemma 4 12B or Qwen3.5 9B instead.
- gpt-oss 20B is fast for its size: it’s a mixture-of-experts model with only ~3.6B active parameters per token.
- With 32 GB+ system RAM, the 26-35B MoE models (
gemma4:26b,qwen3.6:35b) can run split across GPU and RAM at decent speeds. - AMD: the RX 9070 XT is on Ollama’s ROCm support list for Linux; on Windows it runs via Vulkan. See AMD & Intel GPUs.
Check your setup in the GPU picker, or see 16GB hardware picks.
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