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Can the NVIDIA GeForce RTX 4090 run Dolphin 3.0 R1 Mistral 24B?

81 ptsGreat (A) · 81 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 19.9 GB of 24.0 GB usable, with an estimated speed of 43.4 tok/s.

Best quant
Q5_K_M
Generation speed
43.4 tok/s
Prompt processing
2,523 tok/s
First token (1K prompt)
0.4 s
Largest safe context
32K

Download Dolphin 3.0 R1 Mistral 24B

Recommended for the NVIDIA GeForce RTX 4090: Q5_K_M. 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 Q5_K_M file (opens in a new tab)

huggingface.co/mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from Cognitive Computations, for fine-tuning, vLLM, Transformers.

huggingface.co/dphn/Dolphin3.0-R1-Mistral-24B (opens in a new tab)

No sign-in needed.

Links checked 6 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.26.2 / 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
Total81.2 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 26.2/45, 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 4090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 12.6 GB 24.0 GB 86.2 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15 GGUF (opens in a new tab)
Q3_K_M 14.7 GB 24.0 GB 67.9 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15 GGUF (opens in a new tab)
Q4_K_M 17.5 GB 24.0 GB 52.5 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 19.9 GB 24.0 GB 43.4 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 22.5 GB 24.0 GB 36.4 tok/s Tight fit 80 ptsGreat (A) · 80 of 100 points: Model quality 26.7/45, Memory fit 24.0/25, Speed 14.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 28.1 GB 24.0 GB 8.0 tok/s Runs with offload 58 ptsGood (B) · 58 of 100 points: Model quality 27.0/45, Memory fit 10.0/25, Speed 6.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 50.3 GB 24.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.