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
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.
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
Dev Signal
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