RTX 3060 12G — what LLMs can it run?
47 of 87 indexed models fit comfortably in 12GB at 4K context, each at the highest-quality quant that still leaves headroom.
The short answer for RTX 3060 12G
12 GB, 360 GB/s GDDR6. 47 of 87 models in this index fit comfortably at 4K context; 49 load at all.
- Biggest that fits
- DeepSeek-Coder-V2-Lite Instruct — 10.1 GB at Q4_K_M — 1.9 GB spare at 4K, so longer context comes out of a thin margin.
- Room to grow
- Qwen2-VL 7B Instruct — 5.5 GB at Q4_K_M — under 60% of the card, which leaves 6.5 GB for a long context window or a second process.
- Speed ceiling
- Llama 3.1 8B Instruct at Q4_K_M reads 4.6 GB of weights per token, so 360 GB/s puts a hard ceiling near 78 tok/s. That is arithmetic on two published numbers, not a benchmark — real throughput lands below it. No run on this card has been measured here, so there is nothing to compare the ceiling against.
- The wall you will hit
- Bandwidth, not capacity. The models on this page fit, but at 360 GB/s this card reads the whole weight set once per generated token — a 912 GB/s RTX 3080 Ti holds exactly the same 12 GB and moves those bytes 2.5× faster. Expect the same quant to generate proportionally slower here.
14B · 15
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| DeepSeek-Coder-V2-Lite Instruct16B-A2.4B | Q4_K_M | 10.08 GB | +1.9 GB | 145Estimated |
| DeepSeek-V2-Lite Chat16B-A2.4B | Q4_K_M | 10.08 GB | +1.9 GB | 142Estimated |
| StarCoder2 15B15B | Q4_K_M | 10.19 GB | +1.8 GB | 92Estimated |
| Qwen3 14B Instruct14B | EXL2 4.65bpw | 9.66 GB | +2.3 GB | 125 |
| Qwen2.5 14B Instruct14B | EXL2 4.65bpw | 9.75 GB | +2.3 GB | — |
| DeepSeek-R1-Distill-Qwen-14B14B | EXL2 4.65bpw | 9.75 GB | +2.3 GB | 128 |
| Phi-4 14B14B | Q4_K_M | 10.17 GB | +1.8 GB | 88Community |
| Phi-3 Medium 14B Instruct14B | Q4_K_M | 9.73 GB | +2.3 GB | 102Estimated |
| Mistral Nemo 12B Instruct12B | Q4_K_M | 8.42 GB | +3.6 GB | 112Estimated |
| Gemma 3 12B IT12B | Q5_K_M | 9.84 GB | +2.2 GB | 92Estimated |
| Stable LM 2 12B Chat12B | Q4_K_M | 8.35 GB | +3.7 GB | 108Estimated |
| Llama 3.2 11B Vision Instruct11B | Q4_K_M | 7.66 GB | +4.3 GB | 88Estimated |
| Solar 10.7B Instruct11B | Q4_K_M | 7.6 GB | +4.4 GB | 125Estimated |
| Falcon 3 10B Instruct10B | Q4_K_M | 7.28 GB | +4.7 GB | 118Estimated |
| Gemma 2 9B Instruct9B | Q4_K_M | 7.3 GB | +4.7 GB | 132Estimated |
7B · 22
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| GLM-4-9B-Chat9B | Q8_0 | 10.16 GB | +1.8 GB | 105Estimated |
| Qwen3-VL 8B Instruct8B | Q8_0 | 10.39 GB | +1.6 GB | 108Estimated |
| Ministral 3 8B Instruct8B | Q8_0 | 10.02 GB | +2 GB | — |
| Qwen2-VL 7B Instruct7B | Q4_K_M | 5.49 GB | +6.5 GB | 72Estimated |
| Granite 3.1 8B Instruct8B | Q4_K_M | 5.88 GB | +6.1 GB | 142Estimated |
| Qwen3 8B Instruct8B | Q6_K | 7.64 GB | +4.4 GB | 122 |
| Llama 3.1 8B Instruct8B | Q8_0 | 9.47 GB | +2.5 GB | 118 |
| Nous Hermes 3 Llama 3.1 8B8B | EXL2 4.65bpw | 5.43 GB | +6.6 GB | 232Estimated |
| Aya 23 8B8B | Q4_K_M | 5.64 GB | +6.4 GB | 145Estimated |
| OpenChat 3.6 8B8B | EXL2 4.65bpw | 5.43 GB | +6.6 GB | 228Estimated |
| DeepSeek-R1-Distill-Llama-8B8B | Q5_K_M | 6.51 GB | +5.5 GB | 128Estimated |
| InternLM2 7B Chat7B | Q4_K_M | 5.45 GB | +6.6 GB | 148Estimated |
| Qwen2.5 7B Instruct7B | Q6_K | 6.77 GB | +5.2 GB | 132 |
| Qwen2.5-Coder 7B Instruct7B | EXL2 4.65bpw | 4.87 GB | +7.1 GB | 248Estimated |
| DeepSeek-R1-Distill-Qwen-7B7B | EXL2 4.65bpw | 4.87 GB | +7.1 GB | 210Estimated |
| Gemma 4 E4B ITE4B | Q8_0 | 8.46 GB | +3.5 GB | — |
| OLMo 2 7B Instruct7B | Q8_0 | 10.3 GB | +1.7 GB | 125Estimated |
| WizardLM-2 7B7B | Q4_K_M | 5.14 GB | +6.9 GB | 152Estimated |
| Mistral 7B Instruct v0.37B | Q6_K | 6.75 GB | +5.3 GB | 135Estimated |
| Zephyr 7B Beta7B | Q6_K | 6.75 GB | +5.3 GB | 132Estimated |
| Gemma 4 E2B ITE2B | Q8_0 | 5.2 GB | +6.8 GB | — |
| Gemma 3 4B IT4B | Q8_0 | 5.05 GB | +7 GB | 145Estimated |
≤3B · 10
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| Qwen3 4B Instruct4B | Q6_K | 4.05 GB | +8 GB | 145 |
| Phi-4 Mini Instruct3.8B | Q8_0 | 4.81 GB | +7.2 GB | 262Estimated |
| Phi-3.5 Mini Instruct3.8B | Q8_0 | 5.89 GB | +6.1 GB | 255Estimated |
| Llama 3.2 3B Instruct3B | Q8_0 | 4.05 GB | +8 GB | 285Estimated |
| Qwen2.5 3B Instruct3B | Q8_0 | 3.59 GB | +8.4 GB | 290Estimated |
| Gemma 2 2B Instruct2B | Q8_0 | 3.34 GB | +8.7 GB | 320Estimated |
| Qwen3 1.7B Instruct1.7B | Q8_0 | 2.39 GB | +9.6 GB | 240Estimated |
| Qwen2.5 1.5B Instruct1.5B | Q8_0 | 1.83 GB | +10.2 GB | 410Estimated |
| Llama 3.2 1B Instruct1B | Q8_0 | 1.51 GB | +10.5 GB | 450Estimated |
| Qwen2.5 0.5B Instruct0.5B | Q8_0 | 0.6 GB | +11.4 GB | 540Estimated |
How this list is built
Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly.
A further 2 models load but with no headroom to spare (up to 105% of VRAM) — the Quant Hub’s GPU chips count those too, which is why its number is higher.
Measured on this card
No benchmark runs in this index were recorded on a RTX 3060 12G. Every figure on this page is calculated from the model architecture and the quant level — treat them as estimates, not measurements. The speed column in the tables above is an RTX 4090 figure shown for reference — it is not a speed on this card.
Cards with the same budget
What fits is decided by memory, so every 12GB card of this type returns the same list. These pages are not different answers — they differ in throughput, which this index does not measure per card.
Stepping up
A RTX 5080 (16GB) fits 6 more of the indexed models than this card. RTX 5080 →
Common questions
What is the best local LLM for an RTX 3060 12G?
For everyday use, Qwen2-VL 7B Instruct at Q4_K_M — about 5.5 GB of the card's 12 GB at 4K context, leaving 6.5 GB for a longer window. If you want the largest thing that will load, that is DeepSeek-Coder-V2-Lite Instruct at Q4_K_M (10.1 GB, 1.9 GB spare). "Best" here means best fit for the memory budget — this index does not run task benchmarks, so it cannot tell you which model is smarter.
Can an RTX 3060 12G run InternLM2 20B Chat?
No. Its smallest build here, Q4_K_M, needs about 13.4 GB and this card has 12.0 GB usable — short by 1.4 GB before any context beyond 4K. The largest model this card does clear is DeepSeek-Coder-V2-Lite Instruct.
How many tokens per second does an RTX 3060 12G do on an 8B model at Q4?
This index has no measured run on this card, so it does not publish a figure. What can be stated from specifications: generating a token requires reading every weight once, Llama 3.1 8B Instruct at Q4_K_M is 4.6 GB of weights, and this card moves 360 GB/s — a ceiling near 78 tok/s. Batch size, context length, the runtime and how much of the model sits in cache all take you below it.
RTX 3060 12G or RTX 3080 Ti for local LLMs?
They hold the same models: 12 GB against 12 GB fits 47 and 47 of 87 respectively at 4K. The difference is throughput — 360 GB/s against 912 GB/s, a 2.5× gap in how fast the weights can be read, which is what token generation is bound by. The RTX 3080 Ti generates faster on any model both can hold.
47 of 87 indexed models fit comfortably in 12GB at 4K context, each at the highest-quality quant that still leaves headroom.
This page's figures change when the model or the runtime does.Last updated 2026-10-02 RSS → /feed.xml