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Can the Other Android device (4 GB RAM) run Gemma 3 1B?

16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q3_K_M it needs about 1.1 GB of 1.8 GB usable, with an estimated speed of 39.8 tok/s.

Best quant
Q3_K_M
Generation speed
39.8 tok/s
Prompt processing
68.9 tok/s
First token (1K prompt)
14.9 s
Largest safe context
32K

Best app to run it on the Other Android device (4 GB RAM)

On Android

PocketPal AI

Mobile app · runs on llama.cpp

  • Runs the model on the device itself, offline: download it once in the app.

Get it on Google Play Then download Gemma 3 1B in the app

  • ChatterUI Runs the model on the device itself, offline: download it once in the app.

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 1B

On a phone, tablet or other mobile device you install a chat app and get the model file through it, so there are no commands to copy. Use gemma-3-1b-it-Q3_K_M.gguf for the Other Android device (4 GB RAM).

On Android

PocketPal AI

Free mobile app · runs the model on the device with llama.cpp

Get it on Google Play

  1. Install PocketPal AI (free) and open it.
  2. Tap the menu (☰), then Models, then + and Add from Hugging Face.
  3. Search for unsloth/gemma-3-1b-it-GGUF, open it and download gemma-3-1b-it-Q3_K_M.gguf.
  4. Tap Load next to the model and start a chat. It runs on the device, with no connection needed from then on.

Also works: ChatterUI

Files and commands for a computer

Recommended for the Other Android device (4 GB RAM): Q3_K_M. 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 gemma3:1b

Quantized files · GGUF

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

Download the Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/gemma-3-1b-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-3-1b-it-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/google/gemma-3-1b-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 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_K_M, 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 great
How well it runs on the Other Android device (4 GB RAM): 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.7.6 / 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.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
Match score62.1 / 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 Other Android device (4 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 1.1 GB 1.8 GB 50.6 tok/s Runs great 14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Q2_K
Q3_K_M 1.1 GB 1.8 GB 39.8 tok/s Runs great 16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Q3_K_M
Q4_K_M 1.3 GB 1.8 GB 30.8 tok/s Runs great 19Basic quality (D) · 19 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Q4_K_M
Q5_K_M 1.4 GB 1.8 GB 25.5 tok/s Runs well 19Basic quality (D) · 19 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Q5_K_M
Q6_K 1.5 GB 1.8 GB 21.4 tok/s Runs well 19Basic quality (D) · 19 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Q6_K
Q8_0 1.7 GB 1.8 GB 15.7 tok/s Runs welltight fit 20Basic quality (D) · 20 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. Q8_0
F16 2.7 GB 1.8 GB — Won't runover the app memory limit 20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. BF16

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Gemma 3 1B need on the Other Android device 4GB?

At Q3_K_M it needs about 1.1 GB. The Other Android device 4GB has 1.8 GB usable.

How fast is Gemma 3 1B on the Other Android device 4GB?

About 39.8 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 3 1B should I use on the Other Android device 4GB?

Q3_K_M. 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.

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