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Can the NVIDIA RTX A5500 run Gemma 4 12B?

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

Yes, it runs well

Runs well. At Q8_0 it needs about 15.1 GB of 24.0 GB usable, with an estimated speed of 40.1 tok/s.

Best quant
Q8_0
Generation speed
40.1 tok/s
Prompt processing
2,053 tok/s
First token (1K prompt)
0.5 s
Largest safe context
113K

Download Gemma 4 12B

Recommended for the NVIDIA RTX A5500: Q8_0. 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-it-q8_0

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 Q8_0 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:Q8_0

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 6 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.37.4 / 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.14.6 / 15
Total91.9 / 10091 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

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA RTX A5500

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 7.3 GB 24.0 GB 129 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 8.3 GB 24.0 GB 102 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 31.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.0/15 GGUF (opens in a new tab)
Q4_K_M 9.8 GB 24.0 GB 78.8 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 35.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q5_K_M 11.0 GB 24.0 GB 65.1 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.5/15 GGUF (opens in a new tab)
Q6_K 12.3 GB 24.0 GB 54.7 tok/s Runs well 88 ptsExcellent (S) · 88 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.8/15 GGUF (opens in a new tab)
Q8_0 15.1 GB 24.0 GB 40.1 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 GGUF (opens in a new tab)
F16 26.4 GB 24.0 GB 9.0 tok/s Runs with offload 69 ptsGood (B) · 69 of 100 points: Model quality 37.4/45, Memory fit 10.0/25, Speed 7.1/15, 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.