Gemma 4
5 published articles
Google DeepMind
Gemma 4 just made every other open-weight model look 10x too big
Google DeepMind's Gemma 4 natively multimodal open-weight family introduces thinking mode, encoder-free architecture, and MoE options. The 2.3B model matches Gemma 3's 27B performance. The 31B model tops open-weight leaderboards.
2026-07-13
Open Weights Analysis
Gemma 4 is not a chatbot, and that's the point
Google DeepMind's Gemma 4 is an open-weight model designed for self-hosting and customization, not consumer chat. This analysis compares it to ChatGPT, Claude, and Qwen-3.5 across licensing, privacy, and deployment flexibility, revealing why it matters for regulated industries and on-device AI.
2026-07-10
Google DeepMind
Google DeepMind's Gemma 4 turns 26 billion parameters into a reasoning machine that fits on one GPU
Google DeepMind's Gemma 4 technical report details a family of open-weight models with mixture-of-experts, 1M-token context windows, and multi-modal vision. The release signals a strategic play to bring frontier-level reasoning to developers without the cost of proprietary APIs.
2026-07-09
Mobile AI
Gemma 4 goes fully offline on mobile, no cloud required
React Native developers can now embed Gemma 4 for offline inference with hardware acceleration on both Android and iOS. The model handles vision and tool-use tasks locally, as demonstrated by reading a flyer and scheduling a calendar event entirely on-device.
2026-07-09
Performance
Gemma 4 runs 90% faster in Ollama 0.31 with a trick that needs no config
Ollama 0.31 introduces multi-token prediction for Gemma 4 on Apple Silicon, achieving near 90% faster token generation on coding benchmarks. The speedup comes from an auto-tuned draft model and a custom MLX kernel that eliminates redundant weight reads.
2026-06-29