Check my GPU

on

Can the NVIDIA GeForce RTX 4090 run Gemma 3 270M?

44 ptsUsable (C) · 44 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15

Yes, it runs well

Runs well. At F16 it needs about 2.4 GB of 24.0 GB usable, with an estimated speed of 1,075 tok/s.

Best quant
F16
Generation speed
1,075 tok/s
Prompt processing
220,267 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Gemma 3 270M

Recommended for the NVIDIA GeForce RTX 4090: F16. 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 gemma3:270m-it-fp16

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 F16 file (opens in a new tab)

huggingface.co/unsloth/gemma-3-270m-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-3-270m-it-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/google/gemma-3-270m-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

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 7 days ago against Hugging Face, Ollama and GitHub.

How the C 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.2.7 / 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.2.3 / 15
Total45.0 / 10044 ptsUsable (C) · 44 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15

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

Every quantization on the NVIDIA GeForce RTX 4090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 1.9 GB 24.0 GB 7,526 tok/s Runs well 43 ptsUsable (C) · 43 of 100 points: Model quality 1.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q3_K_M 2.0 GB 24.0 GB 5,925 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q4_K_M 2.0 GB 24.0 GB 4,583 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q5_K_M 2.0 GB 24.0 GB 3,786 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.9/15 GGUF (opens in a new tab)
Q6_K 2.1 GB 24.0 GB 3,180 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.0/15 GGUF (opens in a new tab)
Q8_0 2.1 GB 24.0 GB 2,333 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.0/15 GGUF (opens in a new tab)
F16 2.4 GB 24.0 GB 1,075 tok/s Runs well 44 ptsUsable (C) · 44 of 100 points: Model quality 2.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.3/15 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.