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Can the NVIDIA GeForce RTX 4080 SUPER run Magistral Small 1.2 (24B)?

81 ptsGreat (A) · 81 of 100 points: Model quality 27.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 14.5 GB of 16.0 GB usable, with an estimated speed of 48.6 tok/s.

Best quant
Q3_K_M
Generation speed
48.6 tok/s
Prompt processing
1,565 tok/s
First token (1K prompt)
0.7 s
Largest safe context
16K

Download Magistral Small 1.2 (24B)

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run magistral:24b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

huggingface.co/mistralai/Magistral-Small-2509-GGUF (opens in a new tab)

ollama run hf.co/mistralai/Magistral-Small-2509-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Magistral-Small-2509 (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.27.2 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total81.2 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 27.2/45, Memory fit 24.0/25, 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 4080 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 12.3 GB 16.0 GB 61.8 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 14.5 GB 16.0 GB 48.6 tok/s Tight fit 81 ptsGreat (A) · 81 of 100 points: Model quality 27.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 17.4 GB 16.0 GB 19.3 tok/s Runs with offload 66 ptsGood (B) · 66 of 100 points: Model quality 30.8/45, Memory fit 10.0/25, Speed 10.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 19.8 GB 16.0 GB 9.6 tok/s Runs with offload 63 ptsGood (B) · 63 of 100 points: Model quality 31.4/45, Memory fit 10.0/25, Speed 7.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 22.4 GB 16.0 GB 6.1 tok/s Heavy offload 56 ptsUsable (C) · 56 of 100 points: Model quality 32.1/45, Memory fit 4.0/25, Speed 5.9/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 28.2 GB 16.0 GB 3.3 tok/s Heavy offload 55 ptsUsable (C) · 55 of 100 points: Model quality 32.4/45, Memory fit 4.0/25, Speed 4.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 50.8 GB 16.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.