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Can the NVIDIA GeForce RTX 4070 SUPER run MiniCPM4 8B?

79 ptsGreat (A) · 79 of 100 points: Model quality 24.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 8.2 GB of 12.0 GB usable, with an estimated speed of 52.4 tok/s.

Best quant
Q6_K
Generation speed
52.4 tok/s
Prompt processing
3,121 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download MiniCPM4 8B

Quantized files · GGUF

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

huggingface.co/openbmb/MiniCPM4-8B-GGUF (opens in a new tab)

ollama run hf.co/openbmb/MiniCPM4-8B-GGUF

Original weights · Hugging Face

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

huggingface.co/openbmb/MiniCPM4-8B (opens in a new tab)

No sign-in needed.

Links checked 7 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.24.1 / 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
Total79.1 / 10079 ptsGreat (A) · 79 of 100 points: Model quality 24.1/45, 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 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.7 GB 12.0 GB 124 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 17.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.1/15 GGUF files (opens in a new tab)
Q3_K_M 5.5 GB 12.0 GB 97.7 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 20.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.5/15 GGUF files (opens in a new tab)
Q4_K_M 6.5 GB 12.0 GB 75.5 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 23.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.4/15 GGUF (opens in a new tab)
Q5_K_M 7.3 GB 12.0 GB 62.4 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 23.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.0/15 GGUF files (opens in a new tab)
Q6_K 8.2 GB 12.0 GB 52.4 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 24.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 10.1 GB 12.0 GB 38.5 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 14.7/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 17.8 GB 12.0 GB 5.3 tok/s Heavy offload 48 ptsUsable (C) · 48 of 100 points: Model quality 24.3/45, Memory fit 4.0/25, Speed 5.5/15, Memory use 15.0/15 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.