NVIDIA Expands Dynamo AI Inference Platform with Cloud Kubernetes Integration

Details

• NVIDIA announced integrated support for its Dynamo distributed inference platform across managed Kubernetes services from Amazon Web Services, Google Cloud, and Oracle Cloud Infrastructure, enabling enterprises to scale AI model serving across dozens or hundreds of GPU nodes in production environments.
• All major cloud providers are now rolling out Dynamo: AWS integrated it with Amazon EKS, Google Cloud created optimized recipes for its AI Hypercomputer, OCI enabled it on OCI Superclusters, and ecosystem partners like Nebius have adopted the framework for inference-focused cloud infrastructure.
• Dynamo implements disaggregated serving—splitting model inference into independent prefill (prompt processing) and decode (output generation) phases—coupled with NVIDIA Grove, a new orchestration API that lets developers declare full inference requirements in a single specification, automating cluster coordination, scaling, and component placement.
• This marks a strategic shift from single-node to distributed multi-node inference, positioning NVIDIA to dominate the inference orchestration layer much as it controls AI training infrastructure through Kubernetes-native solutions.
• Production deployments show concrete benefits: Baseten achieved 2x inference speedup and 1.6x throughput gains for long-context code generation without hardware additions; SemiAnalysis benchmarks confirm Dynamo on NVIDIA GB200 NVL72 systems delivers the lowest cost per million tokens for reasoning models like DeepSeek-R1.

Impact

NVIDIA is consolidating the distributed-inference orchestration layer across all major cloud platforms, establishing the same strategic position it holds in AI training. Embedding Dynamo into AWS EKS, Google Cloud, and OCI raises switching costs for enterprise customers and accelerates Blackwell GPU adoption. The result: enterprises can deploy sophisticated reasoning models at lower costs per token, faster time-to-production, and reduced operational overhead—economically validating multi-agent AI at enterprise scale.