Can the Arduino UNO Q (2 GB) run Gemma 3 270M?

Yes, it runs well

Runs well. At Q2_K it needs about 0.8 GB of 1.2 GB usable, with an estimated speed of 17.9 tok/s.

Best quant: Q2_K
Generation speed: 17.9 tok/s
Prompt processing: 28.4 tok/s
First token (1K prompt): 36.0 s
Largest safe context: 32K

Best app to run it on the Arduino UNO Q (2 GB)

On Linux on ARM boards

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run gemma3:270m

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Gemma 3 270M

Recommended for the Arduino UNO Q (2 GB): Q2_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.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run gemma3:270m

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K 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:Q2_K

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)

Google DeepMind’s official Gemma library.

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

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 4Basic quality (D) · 4 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs well: How well it runs on the Arduino UNO Q (2 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	1.9 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	10.0 / 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
Match score	51.9 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Arduino UNO Q (2 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	0.8 GB	1.2 GB	17.9 tok/s	Runs well	4Basic quality (D) · 4 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	0.9 GB	1.2 GB	14.1 tok/s	Runs well	5Basic quality (D) · 5 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	0.9 GB	1.2 GB	10.9 tok/s	Runs well	5Basic quality (D) · 5 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	0.9 GB	1.2 GB	9.0 tok/s	Slow	5Basic quality (D) · 5 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	1.0 GB	1.2 GB	7.6 tok/s	Slow	5Basic quality (D) · 5 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	1.0 GB	1.2 GB	5.6 tok/s	Slow	6Basic quality (D) · 6 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	1.3 GB	1.2 GB	2.2 tok/s	Very slowover memory limit	6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 31/100 (quality, fit, speed and memory use) sets the ranking.	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

6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 290 tok/s · Runs great
6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 62.7 tok/s · Runs great
6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) F16 · 110 tok/s · Runs great
6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (24 GB) F16 · 110 tok/s · Runs great
6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 251 tok/s · Runs great
6Basic quality (D) · 6 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 372 tok/s · Runs great

Quick answers

How much memory does Gemma 3 270M need on the Arduino UNO Q 2GB?

At Q2_K it needs about 0.8 GB. The Arduino UNO Q 2GB has 1.2 GB usable.

How fast is Gemma 3 270M on the Arduino UNO Q 2GB?

About 17.9 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 3 270M should I use on the Arduino UNO Q 2GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.