8 GB, 12 GB or 16 GB: what changes for local AI?
More VRAM gives a model, its context cache and runtime more room. It does not automatically make the model's answers better. Compare the same build first.
Same builds, same context, different budgets
All scenarios use 4,096 tokens, Q4_K_M, Windows 11, a working Vulkan backend, 32 GB separate system RAM, an illustrative 8-core/16-thread CPU and 500 GB free disk. Available memory and driver version are unknown. The engine applies reserves. This comparison covers one loaded model and one GPU.
| Build / est. memory | 8 GB | 12 GB | 16 GB |
|---|---|---|---|
| llama3.2:3b 2.9 GB Build source · catalog 2026-09-13 | Excellent fit high confidence | Excellent fit high confidence | Excellent fit high confidence |
| llama3.1:8b 5.8 GB Build source · catalog 2026-09-13 | Excellent fit high confidence | Excellent fit high confidence | Excellent fit high confidence |
| qwen2.5-coder:7b 5.3 GB Build source · catalog 2026-09-13 | Excellent fit high confidence | Excellent fit high confidence | Excellent fit high confidence |
| qwen2.5:14b 10.2 GB Build source · catalog 2026-09-13 | Runs with trade-offs high confidence | Good fit high confidence | Excellent fit high confidence |
How to choose between them
- 8 GB: start with a task-appropriate compact build and check the remaining headroom. If your desired context crosses the memory budget, try a smaller build before buying hardware.
- 12 GB: compare using the extra room for conversation length versus a higher-bit quantization. Keep one setting fixed so you can see why the estimate changes.
- 16 GB: gives more room for larger builds or context, but this is dedicated GPU memory. A 16 GB Mac shares its pool with macOS and CPU allocations.
Try the same model at 4K and 16K context
Open a build in the calculator, record its total at 4,096 tokens, then choose 16,384. The weights stay the same while the context cache changes. If architecture metadata is missing, that cache estimate has lower confidence. Stay within the model's documented context limit.
Check a real run with ollama ps and the runtime logs. A memory-fit table is not a purchasing benchmark; compare real measurements with the same model, settings and runtime before paying for a speed upgrade.
Maintained by Delcio Pedro. Guide reviewed 15 September 2026; catalog dates are shown with the builds. These are reference calculations, not inference benchmarks. Methodology.