Best LLMs for 12 GB VRAM
Mid-range (RTX 3060, RTX 4070, RTX 5070) — 7-13B models at Q4-Q6
12 GB is the sweet spot for entry into local AI. It runs 7B–13B models at good quality quantizations, making it a practical and affordable starting point for running LLMs on your own hardware.
This memory tier, common on GPUs like the RTX 3060 12GB, is surprisingly capable for local AI. You can run Llama 3 8B, Mistral 7B, and similar 7B models at Q4_K_M quantization with decent token generation speed. Smaller models like Phi 3 Mini (3.8B) run at Q6 or Q8 with room to spare. Reaching up to 13B models is possible at Q2–Q3 quantization, though quality trade-offs become more noticeable.
Runs Well
- 7B models at Q4_K_M quality
- Small models (3B–4B) at Q5–Q8
- Chat and coding assistants for everyday use
Challenging
- 13B models only at Q2–Q3 (lower quality)
- 14B+ models do not fit
- Context windows limited for 7B+ models
GPUs with ~12.0 GB VRAM
All 11 GPUsNVIDIA GeForce RTX 3080
NVIDIA · Ampere
NVIDIA GeForce GTX 1080 Ti
NVIDIA · Pascal
NVIDIA GeForce RTX 5070
NVIDIA · Blackwell
NVIDIA GeForce RTX 3080 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 3060 12GB
NVIDIA · Ampere
NVIDIA GeForce RTX 4070 Ti
NVIDIA · Ada Lovelace
Models That Fit in 12 GB VRAM
Speed estimated for NVIDIA GeForce RTX 3080 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 4.9 GB41% | 120.5 t/s | 33K | FAIR FIT | B56 | |
| Q4_K_M | 5.0 GB42% | 118.8 t/s | 131K | FAIR FIT | B57 | |
| Q8_0 | 4.9 GB41% | 120.8 t/s | 4K | FAIR FIT | B56 | |
| Q4_K_M | 2.9 GB24% | 205.2 t/s | 41K | EASY RUN | C39 | |
| Q4_K_M | 2.6 GB22% | 224.6 t/s | 2K | EASY RUN | C37 | |
| Q4_K_M | 2.9 GB24% | 208.1 t/s | 131K | EASY RUN | C39 | |
| Q4_K_M | 2.0 GB17% | 299.5 t/s | 131K | EASY RUN | C34 | |
| Q4_K_M | 1.0 GB8% | 587.2 t/s | 2K | EASY RUN | D29 |
Frequently Asked Questions
- What models can I run with 12.0 GB VRAM?
With 12.0 GB VRAM, you can run 7B-13B models at good quality quantizations like Q4_K_M, with smaller models running at Q6 or Q8.
- Is 12.0 GB enough for local AI?
12.0 GB is a practical entry point for local AI. You can run the most popular 7B models like Llama 3 8B and Mistral 7B at good quality, making it an affordable starting tier.
- What GPU should I get for 12.0 GB VRAM?
There are several GPUs with approximately 12.0 GB VRAM at different price points. Popular choices include NVIDIA GeForce RTX 3080, NVIDIA GeForce GTX 1080 Ti, NVIDIA GeForce RTX 5070. Memory bandwidth also matters — higher bandwidth means faster token generation. Check the GPU cards above for specific specs and pricing.
- What quantization works best with 12.0 GB?
For 12.0 GB, Q4_K_M is typically the best starting quantization — it offers a good balance of model quality and VRAM usage. For smaller 3B-4B models, you can use Q6_K or Q8 for higher quality. Use Q2_K or Q3_K_M only when you need to squeeze in a model that's otherwise too large.