Can the RTX A1000 run Mistral-7B-Instruct-v0.2?

Yes. Mistral-7B-Instruct-v0.2 fits the RTX A1000's 8 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 28 tok/s. There's room for up to Q5_K_M if you want higher quality.

VRAM
8 GB
Needs at Q4
5.8 GB
Headroom
2.2 GB
Speed (Q4_K_M)
~28 tok/s

Every quantization

Mistral-7B-Instruct-v0.2 (7.2B) on 8 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M4 GB5.8 GBFits~28 tok/s
Q5_K_M5 GB6.8 GBFits~22 tok/s
Q8_08 GB9.8 GBToo big -
FP1614 GB15.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX A1000's 192 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). 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 A1000

Frequently asked

Quick answers

Can the RTX A1000 run Mistral-7B-Instruct-v0.2?

Yes. Mistral-7B-Instruct-v0.2 fits the RTX A1000's 8 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 28 tok/s. There's room for up to Q5_K_M 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 A1000?

Roughly 28 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX A1000's 192 GB/s memory bandwidth.