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Can the NVIDIA GeForce RTX 4080 SUPER run Gemma 4 12B?

91 ptsExcellent (S) · 91 of 100 points: Model quality 37.0/45, 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 11.9 GB of 16.0 GB usable, with an estimated speed of 52.4 tok/s.

Best quant
Q6_K
Generation speed
52.4 tok/s
Prompt processing
3,142 tok/s
First token (1K prompt)
0.3 s
Largest safe context
57K

Download Gemma 4 12B

Recommended for the NVIDIA GeForce RTX 4080 SUPER: Q6_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

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

ollama run gemma4:12b

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.

Download the Q6_K file (opens in a new tab)

huggingface.co/unsloth/gemma-4-12B-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-4-12B-it-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/google/gemma-4-12B-it (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/google-deepmind/gemma (opens in a new tab)

git clone https://github.com/google-deepmind/gemma

Google DeepMind’s official Gemma library.

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

How the S 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.37.0 / 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
Total92.0 / 10091 ptsExcellent (S) · 91 of 100 points: Model quality 37.0/45, 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 4080 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.9 GB 16.0 GB 124 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 7.9 GB 16.0 GB 97.7 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 31.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.4/15 GGUF (opens in a new tab)
Q4_K_M 9.4 GB 16.0 GB 75.5 tok/s Runs well 88 ptsExcellent (S) · 88 of 100 points: Model quality 35.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.5/15 GGUF (opens in a new tab)
Q5_K_M 10.6 GB 16.0 GB 62.4 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 11.9 GB 16.0 GB 52.4 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 14.7 GB 16.0 GB 38.5 tok/s Tight fit 91 ptsExcellent (S) · 91 of 100 points: Model quality 37.4/45, Memory fit 24.0/25, Speed 14.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 26.0 GB 16.0 GB 3.4 tok/s Heavy offload 60 ptsGood (B) · 60 of 100 points: Model quality 37.4/45, Memory fit 4.0/25, Speed 4.4/15, 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.