Gemma 4 Drops Four Open Models, and the 31B Dense Beats a 397B MoE

Google DeepMind released Gemma 4 on April 2 with four model sizes (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0. Multimodal (text, vision, audio). 256K context. Native thinking and tool-calling optimized for agentic workflows. Day-zero ecosystem support across vLLM, llama.cpp, Ollama, and Unsloth.

The numbers that matter: the 26B MoE hits 162 tok/s on an RTX 4090 at 19.5GB VRAM and 34 tok/s on a Mac mini M4. Only 3.8B parameters active at inference time, which is why it runs like a 4B model while thinking like a 26B one.

But the result that caught me off guard came from the community. On FoodTruck Bench, Gemma 4 31B dense placed 3rd overall, beating GLM 5, Qwen 3.5 397B (a model 15x its size), and every Claude Sonnet variant. A 31B model outperforming a 397B MoE. One year ago, DeepSeek R1 launched at 671B parameters for comparable performance. That's 25x compression in 12 months.

The r/LocalLLaMA community also noticed something benchmarks don't capture: Gemma 4 admits when it doesn't know things instead of hallucinating confidently. Qwen 3.5 fabricates answers with great confidence. For production use, honest uncertainty beats confident hallucination every time.

One caveat from day-1 testing: the 31B model at 256K context needs ~22GB just for KV cache on top of model weights. Google didn't adopt KV-reducing techniques from Qwen 3.5. On a 24GB Mac, you're hitting swap. The 26B MoE is the real sweet spot for local deployment.

Someone also got the 26B running on a Rockchip NPU at 4 watts of power. Apache 2.0 licensing means you can ship this in production today. If you're building anything with local inference, test Gemma 4 this week.