Can the RTX 3090 run Mistral-7B-Instruct-v0.2?

Yes. Mistral-7B-Instruct-v0.2 fits the RTX 3090's 24 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 137 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
24 GB
Needs at Q4
5.8 GB
Headroom
18.2 GB
Speed (Q4_K_M)
~137 tok/s

Every quantization

Mistral-7B-Instruct-v0.2 (7.2B) on 24 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M4 GB5.8 GBFits easily~137 tok/s
Q5_K_M5 GB6.8 GBFits easily~110 tok/s
Q8_08 GB9.8 GBFits easily~68 tok/s
FP1614 GB15.8 GBFits~39 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 3090's 936 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Mistral-7B-Instruct-v0.2 details →

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

Mistral-7B-Instruct-v0.2 on other hardware

Also runs on the RTX 3090

Frequently asked

Quick answers

Can the NVIDIA RTX 3090 run Mistral-7B-Instruct-v0.2?

Yes. Mistral-7B-Instruct-v0.2 fits the RTX 3090's 24 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 137 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Mistral-7B-Instruct-v0.2 need?

About 4 GB of weights at Q4_K_M, 8 GB at Q8_0 and 14 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Mistral-7B-Instruct-v0.2 on the RTX 3090?

Roughly 137 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 3090's 936 GB/s memory bandwidth.