AI models · Updated 2026-10-31

Best local AI models to run on a gaming PC

Pick a local model by available GPU memory and task, then verify speed, quality, and license on your own computer.

The short answer

Choose a model that fits comfortably in your GPU memory after accounting for its context and the rest of your desktop. For a mainstream gaming PC, start with a 7B to 12B class model, then test a larger option only if memory and speed allow. The most useful local model is the one that finishes your real task at an acceptable pace, not the one with the biggest parameter count.

Shortlist to test

Ollama’s current library offers several practical starting points. Gemma 4 E4B and 12B are smaller and more approachable than its 26B and 31B workstation choices. Qwen 3.5 has tags from 0.8B through 122B; its 9B package is listed around 6.6–7.6 GB, and the 27B package around 17–20 GB. DeepSeek-R1 includes smaller distilled models as well as much larger options. These downloaded sizes help shortlist a model, but they are not a complete GPU-memory requirement: context state, runtime overhead, and other applications need room too.

For a first install, try an instruction-tuned 7B–12B model such as a compatible Qwen 3.5 or Gemma 4 tag. Use a smaller 2B–4B model for quick classification or simple drafting. Consider Gemma 4 26B or Qwen 3.5 27B only if the package fits alongside the context size you actually need; the library lists their packages at roughly 16–20 GB. A gaming GPU with 16 GB of memory may have little room left for a long prompt, a game, or other GPU work, so a smaller model can be a better experience.

Match model size to the task

• Private notes, basic drafting, classification: begin with a small model. Test whether it follows your format and handles your common edge cases.
• Coding help and structured reasoning: compare a 9B–14B model with a larger option on the same local task. Check whether it can use your tools and repository context; a model’s local availability does not make it as capable as a hosted frontier model.
• Images or mixed inputs: choose a model tag that explicitly lists image input. For example, Ollama’s Gemma 4 library lists text and image support across several sizes.
• Large context: do not set the advertised maximum by default. More context consumes memory; increase it only when the task needs it and the machine stays responsive.

Install and measure before committing

Install a local runner such as Ollama, check its supported GPU page for your card, and download one model tag. Run a short set of representative prompts, then inspect the runner’s process display to see whether the model is fully on the GPU or partly offloaded. Record first-token delay, tokens per second, memory use, and task quality. Repeat while your usual applications are open, because a model that works only on an otherwise idle desktop may not suit daily use.

Open models come with licenses and use conditions. Read the license attached to the exact model family and distribution before commercial use, redistribution, or tuning. Model tags and package sizes change, so treat the library as a live catalog and verify the current tag before downloading. For more consistent quality on harder work, keep a hosted option alongside the small-team model suite.