Google DeepMind’s April 2, 2026 X thread introduced Gemma 4 as a new open model family built for reasoning and agentic workflows. Google says the lineup spans E2B, E4B, 26B MoE, and 31B Dense, and adds native function calling, structured JSON output, and longer context windows.
A LocalLLaMA post drew attention to PokeClaw, an open-source Android prototype that runs Gemma 4 locally through LiteRT-LM and lets the model tap, swipe, type, open apps, send messages, and manage auto-replies without cloud inference.
A Show HN thread highlighted Gemma Gem, a Chrome extension that runs Gemma 4 locally via WebGPU and exposes page-reading, clicking, typing, scrolling, screenshot, and JavaScript tools without API keys or server-side inference.
A LocalLLaMA explainer argues that Gemma 4 E2B/E4B gain their efficiency from Per-Layer Embeddings. The key point is that many of those parameters behave more like large token lookup tables than always-active compute-heavy layers, which changes the inference trade-off.
Reddit picked up Google’s Gemma 4 edge rollout, focusing on Agent Skills in Google AI Edge Gallery and the LiteRT-LM runtime. The main claims are sub-1.5GB memory, a 128K context window, and published benchmarks on Raspberry Pi 5 and Qualcomm NPUs.
A LocalLLaMA thread highlighted Gemma 4 31B's unexpectedly strong FoodTruck Bench showing, and the discussion quickly turned to long-horizon planning quality and benchmark reliability.
A post in r/artificial pointed readers to Google DeepMind's Gemma 4 release, which packages advanced reasoning and agentic features under Apache 2.0. Google says the family spans four sizes, supports up to 256K context in larger models, and ships with day-one ecosystem support from Hugging Face to llama.cpp.
r/LocalLLaMA pushed Gemma 4 into one of the strongest community signals in this crawl as Google shipped an open model family spanning edge devices through workstation-class local servers.
Google said on April 2, 2026 that Gemma 4 is its most capable open model family so far, built from the same technology base as Gemini 3. Google says the family spans E2B, E4B, 26B MoE, and 31B Dense models, adds function-calling and structured JSON support, and offers up to 256K context with an Apache 2.0 license.
Google DeepMind announced Gemma Scope 2, extending open interpretability tooling to the full Gemma 3 family from 270M to 27B parameters. The company says the release involved roughly 110 Petabytes of stored data and over 1 trillion total trained parameters.