Home / Can I run it? / Llama 3.2 3B / Llama 3.2 3B on an RTX 3070 Ti

Yes. Llama 3.2 3B at Q8_0 needs about 4.9 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a ~4k context.

Can I run Llama 3.2 3B on an RTX 3070 Ti?

Llama 3.2 3B at Q8_0 needs about 4.9 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable. That figure is 8 GB of VRAM on the spec sheet, of which 8.0 GB is usable for weights and cache, at 608 GB/s memory bandwidth. A tiny Meta Llama, aimed at phones and low-VRAM cards. Quality is limited; it is the model you pick when memory is tight. These are estimates from our formula, not measured benchmarks.

Quant that fits

Q8_0 Fits with headroom

Memory needed

4.9 GB at Q8_0, ~4k context

Usable VRAM

8.0 GB of 8 GB VRAM

Est. tokens/sec

107–151 estimate

Llama-3.2-3B-Instruct · GeForce RTX 3070 Ti · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 107–151 tokens/sec at Q8_0. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At Q8_0, this machine has headroom out to about 16k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

• See the best GPU for this model family and the Llama-3.2-3B-Instruct model page.

Other hardware for Llama 3.2 3B

• Llama 3.2 3B on an RTX 3060 12GBFits
• Llama 3.2 3B on an RTX 4060 Ti 16GBFits
• Llama 3.2 3B on an RTX 4090Fits
• Llama 3.2 3B on an Arc B580Fits
• Llama 3.2 3B on a Mac mini 16GBFits

Other models on an RTX 3070 Ti

• Qwen3-32B on an RTX 3070 TiWon’t fit
• Qwen3-30B-A3B on an RTX 3070 TiWon’t fit
• Qwen3.8-27B on an RTX 3070 TiWon’t fit
• Gemma 3 27B on an RTX 3070 TiWon’t fit
• Gemma 4 31B on an RTX 3070 TiWon’t fit
• gpt-oss-20b on an RTX 3070 TiWon’t fit