Cloudera and VAST Data Take Aim at GPU Starvation With Joint AI Factory Stack

Cloudera and VAST Data have partnered to build a unified AI factory architecture aimed at eliminating GPU starvation in enterprise AI deployments. The joint stack combines Cloudera's containerized data services with VAST's AI Operating System and NVIDIA's AI Data Platform to keep GPU clusters fed with low-latency data across the full AI lifecycle. The solution is available now for hybrid and multi-cloud environments, targeting regulated industries with on-premises and cloud deployment options.

Cloudera and VAST Data have entered into a partnership to build a unified AI factory architecture for enterprises running continuous AI training, inference, and analytics workloads. The joint offering combines Cloudera’s containerized data services with the VAST AI Operating System, targeting a problem that has become increasingly common in enterprise AI deployments: expensive GPU clusters sitting idle while they wait on data. That idle-GPU problem, often called GPU starvation, tends to show up when organizations bolt AI workloads onto data architectures that were never built for the continuous, high-throughput demands of modern AI pipelines. Data preparation, training, inference, and analytics all compete for the same infrastructure, and when the data layer can’t keep pace, the accelerators stall. Cloudera and VAST are positioning their combined stack as a fix for that bottleneck, aiming to keep GPUs fed with low-latency data throughout the full AI lifecycle. How the Architecture is Built On the Cloudera side, the company brings its lakehouse architecture, which packages data engineering, streaming, analytics, machine learning, and AI services into portable containers that can run across hybrid and multi-cloud environments. That gives customers a consistent operational model regardless of where the workload actually executes. VAST contributes its Disaggregated Shared Everything DASE architecture as the underlying data infrastructure, scaling to exabyte levels while integrating vector database services with NVIDIA cuVS for GPU-accelerated vector indexing and search. The VAST AI OS is built on the NVIDIA AI Data Platform reference design and is designed to take latent enterprise data and turn it into an AI-ready state that can be consumed directly by training and inference pipelines. Cloudera then layers its data engineering, governance, and AI services on top of that foundation. The combined stack is meant to cover the full path from raw data ingestion through model deployment, with consistent operations across data centers, private cloud, and public cloud. Cloudera and VAST say the architecture eliminates GPU starvation through high-bandwidth, low-latency data pipelines, which should, in turn, improve sustained GPU utilization and compute efficiency. The platform is also built to handle structured, unstructured, and multimodal datasets at scale, with enterprise governance and compliance controls intended for private and sovereign AI deployments. NVIDIA Integration and Inference The partnership leans heavily on NVIDIA’s stack. Alongside the AI Data Platform reference design underpinning VAST’s AI OS, the companies are integrating NVIDIA AI Enterprise software into the joint architecture. Cloudera’s AI Inference Service uses NVIDIA NIM microservices to enable organizations to deploy and scale models directly on their own data, including NVIDIA’s newer Nemotron open models. On the data engineering side, customers can accelerate Apache Spark workloads using NVIDIA cuDF, which integrates transparently with Cloudera Data Engineering. That allows Spark jobs to tap into VAST’s high-throughput data services with GPU-accelerated processing, which should help on data-heavy stages of AI pipelines rather than just training and inference. For regulated industries in particular, Cloudera and VAST are framing this as a “silicon-to-application” solution, covering everything from the underlying NVIDIA hardware and software up through production AI applications, deployable on-premises or in the cloud depending on data residency and compliance requirements. Availability The companies say the partnership combines 60 exabytes of customer-managed data across their installed bases, giving both vendors a large pool of existing enterprise customers to target as demand for private AI infrastructure grows. The joint Cloudera-VAST AI factory solution is available now through both companies’ enterprise sales teams and partner networks. Cloudera and VAST plan to expand the portfolio through 2026 with additional reference architectures, validated deployment patterns, and industry-specific solutions.