At Google I/O this year, NVIDIA and Google Cloud announced new resources for their joint developer community: A JAX learning path tailored for NVIDIA GPUs, an NVIDIA Dynamo codelab focused on inference optimizations, and a schedule of monthly developer livestreams to supplement existing curated learning paths, hands‑on labs and events. These additions are intended to accelerate developers’ ability to build and deploy optimized AI applications on NVIDIA infrastructure in Google Cloud.
The materials pair NVIDIA libraries, open models and Google Cloud AI tooling. Developers can use NVIDIA cuDF in Google Colab Enterprise or Dataproc for analytics, combine Google DeepMind’s Gemma 4 with NVIDIA Nemotron and the Google Agent Development Kit to construct multi‑agent applications, and run workloads on Google Cloud G4 VMs equipped with NVIDIA RTX PRO 6000 Blackwell GPUs via Cloud Run or spot instances. NVIDIA Dynamo on GKE is emphasized for optimizing large‑scale inference workloads, including mixture‑of‑experts models.
Over the past year the joint community has evolved into a hub for builders using NVIDIA‑accelerated tools on Google Cloud. Members have produced production‑ready retrieval‑augmented generation applications on GKE, added observability for agent workloads, pursued large language model research, and prototyped hybrid on‑premises and cloud inference for real‑world use cases such as sports analytics and enterprise data pipelines.
NVIDIA and Google Cloud frame the new resources as practical steps toward production readiness. JAX optimizations are presented to support scaling from single‑GPU experiments to multi‑rack deployments and to feed training efforts like the MaxText framework on the Google Cloud AI Hypercomputer, while Dynamo and the codelabs target more efficient, scalable inference serving. The learning path and livestreams provide developers additional hands‑on routes to deploy and tune optimized AI workloads on NVIDIA hardware in Google Cloud.
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