Can the RTX 5090 run Llama 3.1 8B?

Yes. Llama 3.1 8B fits the RTX 5090's 32 GB at Q4_K_M (about 7.3 GB needed), running at an estimated 187 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
32 GB
Needs at Q4
7.3 GB
Headroom
24.7 GB
Speed (Q4_K_M)
~187 tok/s

Every quantization

Llama 3.1 8B (8B) on 32 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M5.5 GB7.3 GBFits easily~187 tok/s
Q5_K_M7 GB8.8 GBFits easily~147 tok/s
Q8_010 GB11.8 GBFits easily~103 tok/s
FP1616 GB17.8 GBFits easily~64 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5090's 1792 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 RTX 5090

Frequently asked

Quick answers

Can the NVIDIA RTX 5090 run Llama 3.1 8B?

Yes. Llama 3.1 8B fits the RTX 5090's 32 GB at Q4_K_M (about 7.3 GB needed), running at an estimated 187 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 RTX 5090?

Roughly 187 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5090's 1792 GB/s memory bandwidth.