Gemma
Gemma is Google DeepMind’s open-model family focused on intelligence-per-parameter and portability. Run capable LLMs on mobile, edge, and PCs, with specialized variants for diffusion, embeddings, translation, medicine, and safety, plus first-class integrations across mainstream ML frameworks and deployment targets.
Overview
Pick a Gemma variant for your constraints, pull weights from Google AI Studio or Hugging Face, then fine-tune or quantize as needed. Run inference through PyTorch, Keras, or JAX, or ship lightweight builds with Ollama or Gemma.cpp for Android, laptops, and edge devices.
Model Family and Capabilities
Gemma suits mobile engineers, edge and IoT builders, startup teams pursuing sovereign or air-gapped deployments, and researchers needing transparent, reproducible baselines. It also fits education and public-sector scenarios where offline operation and predictable resource use matter. Developers who want open weights, portable runtimes, and a breadth of specialized variants will find Gemma practical: rapid prototyping on laptops, local assistants on Android, and efficient batch jobs on modest servers without relying on proprietary endpoints.
- Deploy quantized Gemma variants delivering strong accuracy under tight memory budgets.
- Run on-device inference with Gemma.cpp, Android, and lightweight CPU or GPU backends.
- Select task-specialized families for diffusion, translation, embeddings, and medical interpretation.
- Integrate quickly via PyTorch, Keras, JAX, Hugging Face, Ollama, and AI Studio.
- Apply ShieldGemma 2 classifiers to reduce harmful or policy-violating outputs.
Why Gemma
Who It's For
Start in Google AI Studio to try prompts and evaluate variants. Fetch checkpoints from Hugging Face or Kaggle, then load with PyTorch, Keras, or JAX. Use quantized weights or apply QAT to meet device limits. For lightweight local serving, integrate with Ollama, LM Studio, or compile through Gemma.cpp. Deploy Android builds for on-device assistants, or run scalable inference on Google Cloud. Consult the official documentation and developer forum for examples, safety guidance, and evaluation tips, and benchmark multi-token prediction to tune latency and throughput before moving to production.
Build performant, responsible AI that runs where your users are.
Getting Started
Gemma’s differentiator is practical performance: high intelligence-per-parameter, mobile-first efficiency, and broad, well-supported integrations. The family covers general reasoning and specialized needs without fragmenting tooling. With safety classifiers, translation, embeddings, and medical options, teams can assemble end-to-end pipelines that run locally or in the cloud. Choose Gemma when you need capable, portable open models that respect tight compute budgets.
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Gemma
Google DeepMind is an AI research and engineering organization behind breakthroughs like AlphaGo, AlphaFold, and Gemini. It focuses on building capable, responsible AI systems and tools. The Gemma program extends this mission with open models, developer resources, and safety-first guidance.
Gemma is Google DeepMind’s suite of open models engineered to deliver strong accuracy per parameter and practical deployment anywhere users run software. The Gemma 4 family spans efficient E2B/E4B variants for mobile and IoT, and larger 12B, 26B, and 31B options that bring frontier-like reasoning to personal computers. Developers can choose task-specific lines, including DiffusionGemma for ultra-fast text generation via diffusion, T5Gemma encoder–decoder models for deep contextual understanding, EmbeddingGemma for representation learning, TranslateGemma for multilingual communication, MedGemma for medical text and imaging, and ShieldGemma 2 for safety classification. Recent improvements such as multi-token prediction drafters and quantization-aware training reduce latency and memory, making on-device experiences feasible without major quality loss. Gemma integrates with Google AI Studio for quick trials and ships across popular stacks like PyTorch, Keras, JAX, Hugging Face, Ollama, LM Studio, and Gemma.cpp. The result is an open, portable foundation for building capable, responsible AI systems across cloud, laptops, and constrained devices.
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