What is Google's Gemma 4?

Four open-weight models (E2B, E4B, 26B MoE, 31B dense) from Gemini 3 tech, hybrid attention, edge-optimized, Apache 2.0—runs phones to servers.

How does Gemma 4 compare to Llama 3?

Beats on edge speed/latency, competitive leaderboards (31B third on Arena), freer license; Llama edges general scale but hungrier RAM.

Can Gemma 4 run on a phone or Raspberry Pi?

Yep—E2B does 4k tokens <3s on Pi 5, under 2GB; Android-ready multimodal. Word count: ~950.

🤖 Large Language Models

Google's Gemma 4: The Open Model That Finally Makes Edge AI Less of a Joke

Google dropped Gemma 4 with zero fanfare, but the benchmark leaps and phone-ready smarts have me rethinking open models. Is this the edge AI we've waited for, or just more Google smoke?

theAIcatchup Apr 07, 2026 4 min read

⚡ Key Takeaways

Gemma 4's hybrid attention and Per-Layer Embeddings make edge AI viable now, not hype. 𝕏
Benchmark jumps like 89.2% on AIME signal real progress, Apache 2.0 unlocks enterprise. 𝕏
Google's open play locks in devs long-term, echoing past TensorFlow strategies. 𝕏

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#Gemma 4 #Google AI #edge AI #hybrid attention #open models

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Originally reported by Towards AI

⚡ Key Takeaways

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