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
19 models · 1 excellent · 2 good
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·65.0 t/s tok/s·16K ctx·GREAT FIT | Q4_K_M | 9.1 GB | 65.0 t/s | 16K | GREAT FIT | S89 |
Q4_K_M·74.9 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 7.9 GB | 74.9 t/s | 33K | GOOD FIT | A83 |
Q4_K_M·107.4 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 5.5 GB | 107.4 t/s | 41K | FAIR FIT | B61 |
Q4_K_M·97.2 t/s tok/s·8K ctx·GOOD FIT | Q4_K_M | 6.1 GB | 97.2 t/s | 8K | GOOD FIT | A66 |
Q4_K_M·112.3 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.3 GB | 112.3 t/s | 131K | FAIR FIT | B59 |
Q4_K_M·118.8 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 5.0 GB | 118.8 t/s | 33K | FAIR FIT | B57 |
Q4_K_M·110.4 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.4 GB | 110.4 t/s | 131K | FAIR FIT | B60 |
Q4_K_M·110.0 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.4 GB | 110.0 t/s | 131K | FAIR FIT | B60 |
Q4_K_M·120.5 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 4.9 GB | 120.5 t/s | 33K | FAIR FIT | B56 |
Q4_K_M·118.8 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.0 GB | 118.8 t/s | 131K | FAIR FIT | B57 |
Q8_0·120.8 t/s tok/s·4K ctx·FAIR FIT | Q8_0 | 4.9 GB | 120.8 t/s | 4K | FAIR FIT | B56 |
Q4_K_M·205.2 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 205.2 t/s | 41K | EASY RUN | C39 |
Q4_K_M·224.6 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 224.6 t/s | 2K | EASY RUN | C37 |
Q4_K_M·208.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 208.1 t/s | 131K | EASY RUN | C39 |
Q4_K_M·299.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 299.5 t/s | 131K | EASY RUN | C34 |
Q4_K_M·587.2 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 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 757 LLM models at various quantization levels. Popular models that fit well include Phi 4, Gemma 3 12B IT, Qwen3 8B. 64 models achieve excellent performance at this VRAM level. This is the most popular entry point for local AI. Most 7B models — the workhorse size for chat and coding — fit comfortably.
- Is 12.0 GB enough for local AI?
12.0 GB is a practical entry point for local AI. You can run 757 models, including popular choices like Llama 3 8B and Mistral 7B at good quality. Most users start here — it's enough for a capable local chat assistant that runs entirely on your hardware.
- What GPU should I get for 12.0 GB VRAM?
Popular GPUs with ~12.0 GB include NVIDIA GeForce RTX 3080, NVIDIA GeForce GTX 1080 Ti, NVIDIA GeForce RTX 5070. The NVIDIA GeForce RTX 3080 Ti leads in memory bandwidth at 912.4 GB/s, which translates directly to faster token generation. When choosing a GPU for AI, memory bandwidth matters as much as VRAM capacity — it determines how fast the model can generate text. A newer GPU with the same VRAM but higher bandwidth will produce tokens significantly faster.
Higher memory bandwidth = faster token generation. All these GPUs have approximately 12 GB VRAM, but speed varies significantly by bandwidth.
Memory bandwidth comparison
912.4 GB/s760.3 GB/s672 GB/s504 GB/s504 GB/s- How to choose the right model size for 12.0 GB?
The key rule: your model must fit in VRAM including KV cache overhead. With 12.0 GB, here's a practical guide: 7B models at Q4_K_M are your best bet — good quality and enough room for context. You can push to Q5_K_M for slightly better quality. 13B models barely fit at Q3, which works but quality suffers.
- Should I get 12.0 GB or 16.0 GB for AI?
Upgrading from 12.0 GB to 16.0 GB gives you significantly more flexibility. At 12.0 GB you can run 757 models; the jump to 16 GB is the biggest quality-of-life improvement — it opens up 14B models and lets you use higher quantizations on 7B models. If budget allows, the extra VRAM is always worth it for AI workloads — you can't add VRAM later.