Home / Can I run it? / Llama 3.1 8B / Llama 3.1 8B on an RX 7900 XTX

Yes. Llama 3.1 8B at FP16 needs about 19.5 GB, and the Radeon RX 7900 XTX has 24.0 GB usable at a ~4k context.

Can I run Llama 3.1 8B on an RX 7900 XTX?

Llama 3.1 8B at FP16 needs about 19.5 GB, and the Radeon RX 7900 XTX has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 960 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

FP16 Fits with headroom

Memory needed

19.5 GB at FP16, ~4k context

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

36–51 estimate

Llama-3.1-8B-Instruct · Radeon RX 7900 XTX · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 36–51 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 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 4060 Ti 16GBFits
• Llama 3.1 8B on an RTX 4070 Ti SuperFits
• Llama 3.1 8B on an RTX 4090Fits

Other models on an RX 7900 XTX

• Qwen3-32B on an RX 7900 XTXTight
• Qwen3-30B-A3B on an RX 7900 XTXTight
• Qwen3.8-27B on an RX 7900 XTXFits
• Gemma 3 27B on an RX 7900 XTXFits
• Gemma 4 31B on an RX 7900 XTXTight
• gpt-oss-20b on an RX 7900 XTXFits