gemma-4 inference-optimization multimodal modular-cloud gpu-hardware

Gemma 4 multimodal models ship on Modular Cloud

Google DeepMind's Gemma 4 (31B dense, 26B MoE) now runs on Modular's MAX inference framework with 15% higher throughput than vLLM on NVIDIA B200, supporting 256K context and native video/image processing.

Summary

Eliminates inference framework switching between prototyping and production—same MAX engine handles both, reducing deployment friction for multimodal and long-context workloads. Hardware-agnostic optimization (NVIDIA/AMD) removes vendor lock-in guesswork at scale.

Why it matters

Implementation verdict

Replaces vLLM-based deployments if throughput is your constraint; requires Modular Cloud account or MAX self-hosted setup. Ready now—10-prompt free tier available. Worth trying if you're shipping Gemma 4 or need sub-B200 AMD inference parity.

Sources

1.15% higher throughput when compared to vLLM on NVIDIA B200
2.256K context window
3.only 4B activated per forward pass
4.The same MAX-powered engine that handles your initial tests runs your production Modular Cloud endpoint, so there are no surprises when you scale
5.natively multimodal, supporting text, images, and video with dynamic resolution and aspect ratio

Dev Signal

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