Gemma 3

Gemma 3 4B is a small open-source language model released by Google with approximately 4 billion parameters, capable of running on the CPU of an ordinary consumer-grade laptop.

Gemma 4的1B/2B模型量化后仅需4GB显存即可运行

The Gemma series of models uses the SigLIP vision encoder to process image inputs.

When training Qwen3-MoE, Gemma 3, and Gemma 4 on GPUs that do not support bf16, Unsloth reduces attention memory usage by up to 7 times (requires torch 2.13 or earlier).

IdleWheels uses Gemma 3 (4B parameters) running via Ollama on the owner's own laptop to compare pickup and return photos to detect new damage.

The 4B vision model struggles with receipts that have scrawled cursive handwriting, severe creases, and faded thermal paper.

Shoebox is a local web app that runs the Gemma 3 (4B) vision model locally via Ollama, converts receipt photos into a table exportable as CSV, and keeps all data local.

The author benchmarked on 3 synthetic receipts, achieving 100% accuracy on all four items—date, merchant, total, and tax—with an average of 2.32 seconds per receipt, on hardware consisting of an RTX 4050 laptop GPU (6GB VRAM), a Ryzen 7 7840HS, and 16GB of RAM.

gemma3:1b is the smallest member of Google's Gemma 3 series, with about 1 billion parameters, is an open-weights model that can be downloaded and run offline; the Gemma series is built on technology from the same source as Gemini, uses a decoder-only Transformer architecture, and was trained for multilingual capability and instruction following.

Llama 4, Qwen 3 and Gemma 3 are all in the Ollama model library.

AutoHeal AI is based on the Gemma 3 model and the Ollama local inference runtime, and after downloading the dependencies and model, it can work completely without an internet connection.

Gemma 3 (4B) can run locally on consumer-grade graphics cards, while pure CPU inference requires accepting a certain amount of latency.

FollowUp Local calls the Gemma 3 model through Ollama, running entirely locally with no GPU required.

The Gemma 3 1B small model used by default in FlipPrice has limited generation quality, TabPFN's paid threshold is a breaking point for purely free users, and the lack of real user data makes valuation accuracy questionable.

FlipPrice has been verified on Apple M2 with Python 3.14, and the installation process is to clone the repository, create a venv, pip install, ollama pull gemma3:1b, start uvicorn, and it also provides a one-click Render deployment button.

FlipPrice's description generation uses Gemma 3 (via Ollama) and has a local fallback chain to support offline operation.

Running Gemma 3 locally delivers three benefits: availability in offline environments, privacy protection by keeping photos local, and no per-image billing costs.

Gemma 3 1B has a memory footprint of less than 1 GB and requires only a few hundred milliseconds for inference, making it suitable for offline operation without consuming large amounts of power.

TerraGemma is an offline-first wilderness adventure companion powered by Google's open-weight model Gemma 3 that runs 100% locally.

In practice, AnswerUI is better suited for models with more than 13B parameters or with relatively long native context support, such as Qwen2.5 and Gemma 3.