Home / Can I run it? / Llama 3.1 8B / Llama 3.1 8B on an RTX 4060 Ti 16GB

Yes. Llama 3.1 8B at Q8_0 needs about 10.8 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a ~4k context.

Can I run Llama 3.1 8B on an RTX 4060 Ti 16GB?

Llama 3.1 8B at Q8_0 needs about 10.8 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. Meta’s widely used 8B instruct model, the default small local chat model for a few years running. These are estimates from our formula, not measured benchmarks.

Quant that fits

Q8_0 Fits with headroom

Memory needed

10.8 GB at Q8_0, ~4k context

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

20–29 estimate

Llama-3.1-8B-Instruct · GeForce RTX 4060 Ti 16GB · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 20–29 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 32k 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-3.1-8B-Instruct model page.

Other hardware for Llama 3.1 8B

• Llama 3.1 8B on an RTX 3060 12GBTight
• Llama 3.1 8B on an RTX 3070 TiFits
• Llama 3.1 8B on an RTX 3080Fits
• Llama 3.1 8B on an RTX 4070 Ti SuperFits
• Llama 3.1 8B on an RTX 4090Fits
• Llama 3.1 8B on an RTX 5070 TiFits

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