LLMs ·

Gemma 4 12B

Parameters
12B
Architecture
Dense
Max context
256K tokens
License
apache-2.0
Released
2026-05

How to run it

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.

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

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

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.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps.

KV cache: sliding-window hybrid: the KV estimate counts the global-attention layers plus a small allowance for the sliding-window layers.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 4.8 GB 6.1 GB 8.1 GB GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M Low 5.9 GB 7.1 GB 9.1 GB GGUF (opens in a new tab)
Q4_K_M Good 7.3 GB 8.6 GB 10.6 GB GGUF (opens in a new tab)
Q5_K_M High 8.5 GB 9.8 GB 11.8 GB GGUF (opens in a new tab)
Q6_K High 9.8 GB 11.1 GB 13.1 GB GGUF (opens in a new tab)
Q8_0 Very high 12.7 GB 13.9 GB 16.0 GB GGUF (opens in a new tab)
F16 Full 23.9 GB 25.2 GB 27.2 GB GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Gemma 4 12B

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A580 (launch price $179) at Q3_K_M.

Grade · points Hardware Quant Memory Speed Fit
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 F16 26.8 GB / 32.0 GB 43.2 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 NVIDIA GeForce RTX 4070 Ti SUPER Q6_K 11.9 GB / 16.0 GB 47.9 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 NVIDIA GeForce RTX 4080 SUPER 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 NVIDIA GeForce RTX 5070 Ti Q6_K 11.9 GB / 16.0 GB 63.8 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 NVIDIA GeForce RTX 5080 Q6_K 11.9 GB / 16.0 GB 68.4 tok/s Runs well
91 ptsExcellent (S) · 91 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.6/15 AMD Radeon RX 7900 XTX Q8_0 15.1 GB / 24.0 GB 43.2 tok/s Runs well
91 ptsExcellent (S) · 91 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.6/15 NVIDIA GeForce RTX 3090 Q8_0 15.1 GB / 24.0 GB 48.9 tok/s Runs well
91 ptsExcellent (S) · 91 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.6/15 NVIDIA GeForce RTX 4090 Q8_0 15.1 GB / 24.0 GB 52.7 tok/s Runs well
91 ptsExcellent (S) · 91 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q6_K 11.9 GB / 16.0 GB 39.2 tok/s Runs well
90 ptsExcellent (S) · 90 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 5060 Ti 16GB Q5_K_M 10.6 GB / 16.0 GB 38.0 tok/s Runs well

Check it on your own hardware

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.