RTX 3060 12GB LLM Calculator — What Can It Run?
Check which models fit on a RTX 3060 12GB: max parameters per precision, cache headroom and estimated tokens/sec.
RTX 3060 12GB: 12 GB, 360 GB/s, ~25.6 TFLOPS FP16. The RTX 3060 12GB is the budget local-AI entry point: more VRAM than the 3070/3080 despite being slower, which is exactly the right trade for LLMs — memory capacity gates what runs, bandwidth gates how fast.
Formula
About RTX 3060 12GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the RTX 3060 12GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 12 GB, the largest model it can hold at that quant, and a bandwidth-derived decode-speed estimate (token generation streams the whole model per token, so 360 GB/s is the speed limit that matters). The RTX 3060 12GB is the budget local-AI entry point: more VRAM than the 3070/3080 despite being slower, which is exactly the right trade for LLMs — memory capacity gates what runs, bandwidth gates how fast.
How to use RTX 3060 12GB LLM Calculator — What Can It Run?
- 1Enter your values into RTX 3060 12GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use RTX 3060 12GB LLM Calculator — What Can It Run??
- ✓Computes RTX 3060 12GB LLM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: needed = params × bpw ÷ 8 + reserve.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why is the 3060 12GB better for LLMs than a faster 3070 8GB?+
Because the model must fit before speed matters. 12 GB holds a 13B model at 4-bit (~7.5 GB) with cache room; 8 GB cannot. For local LLMs, VRAM capacity is the first-order spec — compute only decides tokens per second after fitting.
What is the realistic ceiling for an RTX 3060 12GB?+
13B-class models at Q4 (e.g. Llama-2-13B, Qwen2.5-14B tightly) with 2–4K contexts, or 7–8B models at Q5/Q6 with long contexts. At 360 GB/s bandwidth expect roughly 25–35 tokens/s on a 7B Q4 — comfortable for chat.
How is the tokens/sec estimate for the RTX 3060 12GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 360 GB/s peak is achievable, matching llama.cpp benchmarks within ~20%. Prompt prefill is compute-bound and much faster per token.
Why reserve memory beyond the weights?+
The KV cache grows with context (use our per-model KV-cache calculators), CUDA/Metal runtimes take hundreds of MB, and allocator fragmentation wastes more. The default reserve suits 2–8K contexts; long-context work needs significantly more.
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