Best Open Models for 12 GB VRAM in 2026
The 12 GB tier is the value sweet spot: used RTX 3060, RTX 4070, RX 6700 XT. These are the models and quants that actually fit.
Best Open Models for 12 GB VRAM in 2026
12 GB is where local AI gets good: a used RTX 3060, an RTX 4070, or an RX 6700 XT runs a 9B at near-lossless quality and starts to open the 12B class.
The budget
Same rule as the 8 GB guide: plan on ~72% of VRAM for weights, ~1.5–2.5 GB for KV cache and activations. On 12 GB that means ~8.5 GB comfortable, ~10.5 GB tight.
The 12 GB tier list
| Model | Quant | ≈ File size | Intelligence |
|---|---|---|---|
| Qwen3.5 9B | Q8_0 | ~9.9 GB | 55.3 |
| Gemma 4 12B It | Q4_K_M | ~7.0 GB | 54.0 |
| Qwen3.6 27B | IQ3_XXS | ~9.7 GB | 76.5 |
| Nemotron 3 Nano 30B A3B | Q3_K_M | ~13.8 GB | 42.4 (offload) |
The two honest choices
Safe choice — Qwen3.5 9B at Q8_0. Near-lossless, fast, leaves room for a long context. This is the daily driver.
Greedy choice — Qwen3.6 27B at IQ3_XXS. The leaderboard says 76.5 Intelligence and it fits in ~9.7 GB, but you trade context length and some quality. On 12 GB it is a sprint car: fast prefill, short context. If your work is one-shot coding or summaries, it is often worth it; for long agent sessions it is not.
Gemma 4 12B at Q4_K_M splits the difference and is the best Google-flavored option on this tier.
Used-card advice
The used RTX 3060 12 GB remains the best value in local AI: the 12 GB matters more than the newer architecture. A 4070 adds speed; a 5070 Ti 16 GB is the first card we would call "comfortable" for the 16 GB class — see the savings calculator before you spend.
Run it
ollama pull qwen3.5:9b
ollama run qwen3.5:9b
For the 27B: download a GGUF from its model page and
run it with llama.cpp's llama-server -m model.gguf -c 4096.
Bottom line
On 12 GB, start with Qwen3.5 9B Q8_0 and keep a Qwen3.6 27B IQ3_XXS around for short, hard tasks. Do not buy a 12 GB card new for AI; buy used or step up to 16 GB.