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Can the NVIDIA GeForce RTX 4070 SUPER run MedGemma 4B IT?

71 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.5/15

Yes, it runs well

Runs well. At Q8_0 it needs about 6.0 GB of 12.0 GB usable, with an estimated speed of 73.2 tok/s.

Best quant
Q8_0
Generation speed
73.2 tok/s
Prompt processing
5,944 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Download MedGemma 4B IT

Recommended for the NVIDIA GeForce RTX 4070 SUPER: 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/unsloth/medgemma-4b-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/medgemma-4b-it-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/google/medgemma-4b-it (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the B 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.19.8 / 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.11.5 / 15
Total71.3 / 10071 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.5/15

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 3.2 GB 12.0 GB 236 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 13.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF (opens in a new tab)
Q3_K_M 3.5 GB 12.0 GB 186 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 GGUF (opens in a new tab)
Q4_K_M 4.1 GB 12.0 GB 144 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF (opens in a new tab)
Q5_K_M 4.5 GB 12.0 GB 119 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 19.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.6/15 GGUF (opens in a new tab)
Q6_K 5.0 GB 12.0 GB 99.8 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.5/15 GGUF (opens in a new tab)
Q8_0 6.0 GB 12.0 GB 73.2 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.5/15 GGUF (opens in a new tab)
F16 10.0 GB 12.0 GB 33.8 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 13.8/15, 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.