What is Gemma 4 and how do I deploy it?

Google's open MoE model, 26B params. Deploy via llama.cpp on K8s or bare metal. Build from HEAD for support. Use Q4_K_M GGUF for speed.

How fast is Gemma 4 on consumer GPUs like RTX 4090?

96 tok/s single request on dual 5060 Ti. Expect 60-80 on single 4090. MoE efficiency crushes dense models.

Can Gemma 4 fix real production bugs?

Yes, for straightforward issues like K8s GPU scheduling or endpoint leaks. Generates merge-ready code in seconds. Complex logic? Needs human polish.

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Gemma 4: 96 Tokens/Second on Dual RTX Cards, Fixing My Kubernetes Bugs by Lunch

96 tokens per second. That's Gemma 4 chewing through Kubernetes bug reports on my dual RTX setup. Google's open model just turned 'wait and hope' into 'deploy and debug now.'

DevTools Feed Apr 03, 2026 4 min read 47 views

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Gemma 4 inference metrics dashboard showing 96 tok/s on dual RTX GPUs

⚡ Key Takeaways

Gemma 4 hits 96 tok/s on dual RTX consumer hardware, demolishing official benchmarks. 𝕏
From release to production inference: 2 hours, including custom llama.cpp build. 𝕏
Real-world bug fixes in Kubernetes code—production-ready Go and YAML in seconds. 𝕏

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