The RTX 3060 12GB is the budget card that local AI users keep coming back to, and the reason is simple: it has 12 GB of VRAM, which is more than much faster cards such as the RTX 3070, 3080 10GB or 4060. At Q4_K_M it runs 4B and 8B models comfortably, fits Gemma 3 12B, and can just squeeze in a 14B model. Its weakness is memory bandwidth, so it generates text more slowly than newer 12 GB cards.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 3060 12GB specs that matter for local AI
- Memory: 12 GB GDDR6 on a 192-bit bus
- Memory bandwidth: 360 GB/s
- Board power: 170 W
- Watch out: a cut-down RTX 3060 8GB exists with a 128-bit bus and only 240 GB/s of bandwidth. Everything on this page is for the 12 GB model.
VRAM decides which models fit. Bandwidth decides how fast they generate text, because every new token means reading the whole model from memory.
Which AI models fit on the RTX 3060 12GB?
The table assumes Q4_K_M quantization (about 4.9 bits per weight) and an 8K context. Totals include the KV cache and about 1 GB of runtime overhead. Speed is an estimate: memory bandwidth divided by model size, at 60 to 80 percent efficiency, which matches published llama.cpp results on several RTX cards.
| Model | Weights | KV cache (8K) | Total (est.) | On 12 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 88 to 117 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 44 to 59 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 43 to 57 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 29 to 39 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Tight | 24 to 32 tok/s |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | No | – |
| Mistral Small 3.2 24B | 14.7 GB | 1.3 GB | 17.0 GB | No | – |
| Qwen3.6 27B | 17.0 GB | 0.5 GB | 18.6 GB | No | – |
| Gemma 3 27B Instruct | 16.8 GB | 1.1 GB | 18.9 GB | No | – |
| Qwen3 32B | 20.1 GB | 2.1 GB | 23.2 GB | No | – |
| Qwen3.6 35B-A3B | 22.0 GB | 0.2 GB | 23.2 GB | No | – |
“Fits” means the estimate is at most 92 percent of VRAM. “Tight” means it is between 92 and 100 percent, so close other GPU apps. Mixture-of-Experts models (gpt-oss-20b and Qwen3.6 35B-A3B) need their full weights in memory, but they only use part of them per token, so speed is not estimated.
Best models for the RTX 3060 12GB
- Llama 3.1 8B or Qwen3 8B (Q4_K_M or Q5_K_M): the comfortable default. About 7 GB at 8K context leaves room for a longer context or a higher-quality quantization.
- Gemma 3 12B (Q4_K_M): about 9.3 GB at 8K. Gemma 3 uses sliding-window attention on most layers, so its KV cache stays small even with long context.
- Qwen3 4B: about 4.7 GB, fast, and leaves plenty of room for 32K context.
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K, so it fits only just. Use a short context and close other GPU apps.
How context length changes things
The KV cache grows with every token of context, so a model that fits at 4K can fail at 32K.
| Model (Q4_K_M) | Context | KV cache | Total (est.) | On 12 GB |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | 4K | 0.54 GB | 6.5 GB | Fits |
| Llama 3.1 8B Instruct | 8K | 1.07 GB | 7.0 GB | Fits |
| Llama 3.1 8B Instruct | 16K | 2.15 GB | 8.1 GB | Fits |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | Fits |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | Fits |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | Tight |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | No |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | No |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost if you need more context.
What does not fit
Anything from about 20B parameters up is out of reach at Q4: gpt-oss-20b needs about 14 GB, Mistral Small 3.2 24B about 17 GB and the 27B to 32B models 19 to 23 GB. These can run with layers offloaded to system RAM, but expect a large speed drop because system memory is many times slower than VRAM.
Best value for running 8B to 12B models. Nothing else at its price offers 12 GB of VRAM, and that matters more than raw speed for local AI. If you can spend more, a newer 12 GB or 16 GB card gives noticeably faster generation and more room, but the 3060 12GB remains the cheapest way to avoid the 8 GB wall.
Verdict
What speed to expect
With 360 GB/s of bandwidth, an 8B Q4 model has a theoretical ceiling of about 73 tokens per second, and real-world results usually land at 60 to 80 percent of that, so roughly 44 to 59 tokens per second. One community report we found lists about 42 tokens per second for an 8B model on an RTX 3060 12GB at Q4_K_XL and 16K context. It is a single report with an unclear model label, so treat it as a rough check, not a benchmark. It is a little under our range, which is expected for a larger quant and a longer context.
Frequently asked questions
Can the RTX 3060 12GB run Llama 3 8B?
Yes, easily. At Q4_K_M it uses about 7 GB with an 8K context, leaving several GB free.
Can it run a 14B model?
Only just. Qwen3 14B at Q4_K_M needs about 11.4 GB at an 8K context, which is tight on a 12 GB card. Use a short context and no other GPU apps.
Is the RTX 3060 12GB better than the RTX 3070 for AI?
For models that need more than 8 GB, yes. The 3070 is faster in speed terms but its 8 GB limits which models fit.
Is the 8GB RTX 3060 the same?
No. The 8GB version has a narrower 128-bit bus and 240 GB/s of bandwidth, so it is both smaller and slower.
Sources and method
- RTX 3060 specs: Mezha.media’s RTX 3060 8GB article (12GB model: 192-bit, 360 GB/s) and the Wikipedia GeForce 30 series table (170 W).
- Community speed: Hardware Corner figure as reported by singhajit.com.
- Linear-attention and sliding-window layers are handled with reduced KV cache; small fixed states in some newer models are not counted.
- Model architecture: each model’s published
config.jsonon Hugging Face (Llama 3.1 8B and Gemma 3 are read from the unsloth mirrors because the original repositories require a login). - VRAM and speed figures are estimates from our calculator, not our own benchmarks. Last updated 3 Nov 2026.
