Overview As enterprises scale their AI and agentic workflows, they require serverless platforms that make data preparation for model training, evaluation, and inference effortless and efficient.
Today, we’re delivering significant enhancements to Dataflow that directly address your top challenges: maximizing compute efficiency for long-running batch jobs and delivering extra inference power for your most demanding AI workloads. We’re thrilled to announce the general availability of /Resume for Dataflow batch jobs as well as support for G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. With these features, you can accelerate your AI development lifecycle and optimize your costs.
Recover wasted compute and increase developer productivity with /Resume for Dataflow batch jobs Dataflow customers frequently run large batch workloads that sometimes run for a few days. When these jobs fail, Dataflow users currently cannot access the data that was already processed before the job failure. Instead, they have to retry the entire job, leading to wasted compute resources and decreased engineering productivity.
In addition to addressing failures from large jobs, Dataflow customers with AI workloads sometimes want to increase the utilization of accelerated compute resources like GPUs and TPUs by dynamically re-allocating them from already running, lower priority Dataflow batch jobs to higher priority workloads like feature engineering and AI inference.
To better support these use cases, we are announcing the GA launch of /Resume for Dataflow batch jobs. Powered by internal Google innovation, this feature enables Dataflow customers to resume their failed long running jobs instead of starting from scratch. It also allows customers to and resume their Dataflow batch jobs based on their respective business requirements.
For more details, see manually a Dataflow job. Accelerate AI inference workloads with NVIDIA RTX PRO 6000 GPUs While Dataflow already supports a wide variety of GPUs and TPUs for accelerating AI inference workloads, we’re taking things a step further by announcing support for G4 VMs powered by
The NVIDIA RTX PRO 6000 Blackwell GPU delivers significant performance gains compared to the NVIDIA L4 GPU, bringing 96GB vGPU memory and 1.6 TB/s of bandwidth. This means that you can perform AI inference right within your Dataflow job using up to 70B+ parameter models. You can do this while continuing to take advantage of native Dataflow ML capabilities like RunInference, right fitting and GPU-enabled autoscaling which make it easy for you to onboard and scale your AI inference jobs without having to manage underlying infrastructure or manually deal with hard problems like tuning and autoscaling.
Take the next step Together, /Resume and RTX PRO 6000 Blackwell GPUs help you optimize your batch job costs while running demanding AI workloads. We’re incredibly excited about Dataflow’s capabilities and the possibilities they unlock for our customers.