Can I Run Llama 3.1 Family on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Superseded model. Llama 3.1 Family has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

Written by Jakub Rusinowski · Last updated July 23, 2024

Yes

Yes — Llama 3.1 8B Instruct at Q8_0 needs about 10.4 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.6 GB spare), at ~28 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~28 tok/s

RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	360 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Llama 3.1 Family on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	17.9 GB	✗ No	—	—	16 GB
Q8_0	10.4 GB	✓ Yes	16K	~28 tok/s	8.5 GB
Q6_K	8.4 GB	✓ Yes	32K	~35 tok/s	6.6 GB
Q5_K_M	7.5 GB	✓ Yes	32K	~39.6 tok/s	5.7 GB
Q4_K_M	6.7 GB	✓ Yes	32K	~45.2 tok/s	4.8 GB
Q3_K_M	5.3 GB	✓ Yes	32K	~59.3 tok/s	3.4 GB
Q2_K	4.5 GB	✓ Yes	32K	~71.6 tok/s	2.6 GB

RTX 3060 12 GB desktop limitations

The budget entry point to local AI: 12 GB runs 7–14B models well and nothing larger without offload.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
12 GB of VRAM on the NVIDIA GeForce RTX 3060 (12GB) at 360 GB/s.
32 GB of system RAM available for CPU offload when a model exceeds VRAM.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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