Can the Radeon RX 7900 XTX run Llama 3.1 8B?

Yes. Llama 3.1 8B fits the Radeon RX 7900 XTX's 24 GB at Q4_K_M (about 7.3 GB needed), running at an estimated 100 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
24 GB
Needs at Q4
7.3 GB
Headroom
16.7 GB
Speed (Q4_K_M)
~100 tok/s

Every quantization

Llama 3.1 8B (8B) on 24 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M5.5 GB7.3 GBFits easily~100 tok/s
Q5_K_M7 GB8.8 GBFits easily~79 tok/s
Q8_010 GB11.8 GBFits easily~55 tok/s
FP1616 GB17.8 GBFits~34 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the Radeon RX 7900 XTX's 960 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

ollama run llama3.1:8b
llama-server -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Llama 3.1 8B details →

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Same model, other hardware - and other models on this card

Llama 3.1 8B on other hardware

Also runs on the Radeon RX 7900 XTX

Frequently asked

Quick answers

Can the AMD Radeon RX 7900 XTX run Llama 3.1 8B?

Yes. Llama 3.1 8B fits the Radeon RX 7900 XTX's 24 GB at Q4_K_M (about 7.3 GB needed), running at an estimated 100 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Llama 3.1 8B need?

About 5.5 GB of weights at Q4_K_M, 10 GB at Q8_0 and 16 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Llama 3.1 8B on the Radeon RX 7900 XTX?

Roughly 100 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the Radeon RX 7900 XTX's 960 GB/s memory bandwidth.