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Can the NVIDIA GeForce RTX 5080 run Ministral 8B (2410)?

75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 11.1 GB of 16.0 GB usable, with an estimated speed of 74.8 tok/s.

Best quant
Q8_0
Generation speed
74.8 tok/s
Prompt processing
5,054 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Ministral 8B (2410)

Recommended for the NVIDIA GeForce RTX 5080: Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q8_0 file (opens in a new tab)

huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Ministral-8B-Instruct-2410-GGUF:Q8_0

Original weights · Hugging Face

Full-precision files from Mistral AI, for fine-tuning, vLLM, Transformers.

huggingface.co/mistralai/Ministral-8B-Instruct-2410 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Mistral AI. The weights themselves download from Hugging Face.

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.20.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total75.7 / 10075 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 5080

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.8 GB 16.0 GB 241 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 GGUF (opens in a new tab)
Q3_K_M 6.5 GB 16.0 GB 190 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 17.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q4_K_M 7.5 GB 16.0 GB 147 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.8/15 GGUF (opens in a new tab)
Q5_K_M 8.3 GB 16.0 GB 121 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 20.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15 GGUF (opens in a new tab)
Q6_K 9.2 GB 16.0 GB 102 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 20.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.2/15 GGUF (opens in a new tab)
Q8_0 11.1 GB 16.0 GB 74.8 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 18.6 GB 16.0 GB 10.7 tok/s Runs with offload 53 ptsUsable (C) · 53 of 100 points: Model quality 20.7/45, Memory fit 10.0/25, Speed 7.8/15, Memory use 15.0/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.