Can the RTX 5080 run Mistral-7B-Instruct-v0.2?
Yes. Mistral-7B-Instruct-v0.2 fits the RTX 5080's 16 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 138 tok/s. There's room for up to FP16 if you want higher quality.
Every quantization
Mistral-7B-Instruct-v0.2 (7.2B) on 16 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 4 GB | 5.8 GB | Fits easily | ~138 tok/s |
| Q5_K_M | 5 GB | 6.8 GB | Fits easily | ~110 tok/s |
| Q8_0 | 8 GB | 9.8 GB | Fits | ~69 tok/s |
| FP16 | 14 GB | 15.8 GB | Tight | ~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 5080'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
llama-server -hf TheBloke/Mistral-7B-Instruct-v0.2-GGUF -ngl 99Ollama 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 →
Compare
Same model, other hardware - and other models on this card
Mistral-7B-Instruct-v0.2 on other hardware
- RTX 5090~257 tok/s
- RTX 5090 D~257 tok/s
- RTX 6000D~225 tok/s
- RTX 4090~156 tok/s
- Radeon RX 7900 XTX~138 tok/s
- RTX 3090~137 tok/s
- RTX 3080 Ti~131 tok/s
- RTX 5080 Mobile~129 tok/s
Also runs on the RTX 5080
- Qwen2.5-Coder-14B-Instruct~61 tok/s
- Qwen3 14B~69 tok/s
- Phi-4~65 tok/s
- Gemma 4 12B~79 tok/s
- Qwen3.5 9B~92 tok/s
- Llama 3.1 8B~100 tok/s
- Qwen3.5 4B~183 tok/s
- Qwen3-4B-Instruct-2507~275 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 5080 run Mistral-7B-Instruct-v0.2?
Yes. Mistral-7B-Instruct-v0.2 fits the RTX 5080's 16 GB at Q4_K_M (about 5.8 GB needed), running at an estimated 138 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 5080?
Roughly 138 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5080's 960 GB/s memory bandwidth.