Home / Can I run it? / Llama 3.2 3B / Llama 3.2 3B on an RTX 4060 Ti 16GB

Yes. Llama 3.2 3B at FP16 needs about 8.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a ~4k context.

Can I run Llama 3.2 3B on an RTX 4060 Ti 16GB?

Llama 3.2 3B at FP16 needs about 8.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 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

FP16 Fits with headroom

Memory needed

8.4 GB at FP16, ~4k context

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

27–38 estimate

Llama-3.2-3B-Instruct · GeForce RTX 4060 Ti 16GB · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 27–38 tokens/sec at FP16. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At FP16, this machine has headroom out to about 64k 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 3070 TiFits
• 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 4060 Ti 16GB

• Qwen3-32B on an RTX 4060 Ti 16GBWon’t fit
• Qwen3-30B-A3B on an RTX 4060 Ti 16GBWon’t fit
• Qwen3.8-27B on an RTX 4060 Ti 16GBWon’t fit
• Gemma 3 27B on an RTX 4060 Ti 16GBWon’t fit
• Gemma 4 31B on an RTX 4060 Ti 16GBWon’t fit
• gpt-oss-20b on an RTX 4060 Ti 16GBTight