Optimizing AI infrastructure performance. The collaboration introduces several technical integrations to improve AI infrastructure efficiency. Notably, VAST selected 6th Gen AMD EPYC processors to power its 6th-generation CBox and 3rd-generation EBox platforms. These processors support PCIe Gen-6, which doubles the input-output bandwidth generationally to improve file and object storage performance. "AI is entering an operational phase where infrastructure efficiency matters as much as model performance." John Mao, Vice President of Global Technology Alliances at VAST Data Furthermore, early testing of the AMD Instinct MI355X GPU showed a 9X speedup in time-to-first-token. The system also achieved 9.7X more token throughput when using VAST for key-value cache offloading. These results are critical for delivering low-latency and energy-efficient deployments. Reference architectures and ecosystem growth. In addition, VAST, AMD, and DriveNets developed a reference architecture featuring AMD Helios rack-scale AI infrastructure. This architecture provides guidance for model training, inference, and reinforcement learning workloads. Meanwhile, software innovators like TensorMesh and EmbeddedLLM are collaborating to accelerate production-ready deployments. "Our expanded collaboration with VAST combines AMD EPYC CPUs and Instinct GPUs with the software foundation customers need to accelerate inference." Derek Dicker, Corporate Vice President at AMD The integration also includes automated key-value cache lifecycle management. This feature uses native data lifecycle policies to automatically delete cached data containing sensitive information. As a result, enterprises can maintain regulatory compliance without manual operational overhead. The system manages these tasks through its core database and event streaming capabilities. Industry adoption and future outlook. Several AI cloud providers, including Core42, Crusoe, and Vultr, are deploying these joint technologies. These companies require architectures that combine accelerated computing with context management. Ultimately, the unified platform helps organizations transition from pilot projects to production environments.

VAST Data, AMD expand collaboration to accelerate enterprise AI infrastructure. VAST Data and AMD have expanded their collaboration to help enterprises build AI infrastructure for large-scale inference and agentic AI. VAST Data and AMD have expanded their collaboration to help AI cloud providers and enterprises build high-performance AI infrastructure designed to support training, inference and agentic AI workloads at scale. The collaboration combines the VAST AI Operating System with 6th Gen AMD EPYC processors and AMD Instinct GPUs, bringing together compute, data management and software to address the growing demands of production AI environments. The companies said the joint platform is intended to improve infrastructure efficiency as organisations deploy increasingly complex inference, retrieval-augmented generation (RAG) and agentic AI applications. The expanded collaboration includes new AI infrastructure reference architectures developed alongside DriveNets, support for AMD's latest EPYC processors in VAST's next-generation platforms, and software integrations with TensorMesh and EmbeddedLLM. It also introduces KV cache optimisation capabilities, automated cache lifecycle management for enterprise compliance, and networking integration using the AMD Pensando Pollara 400 AI NIC. According to VAST Data, early testing using an AMD Instinct MI355X GPU demonstrated a 9X improvement in time-to-first-token and 9.7X higher token throughput through KV cache offloading for high-concurrency agentic AI workloads. "AI is entering an operational phase where infrastructure efficiency matters as much as model performance," said John Mao, Vice President, Global Technology Alliances at VAST Data. "The industry is discovering that inference is fundamentally a data problem. Success depends on how effectively organizations can bring data, compute, memory and intelligence together as a single system." Derek Dicker, Corporate Vice President, Enterprise Business Group at AMD, added: "Our expanded collaboration with VAST combines AMD EPYC CPUs and Instinct GPUs with the software foundation customers need to accelerate inference, improve infrastructure efficiency and deploy AI at scale." According to VAST Data, the collaboration reflects growing demand for AI infrastructure that supports large-scale inference and agentic AI alongside traditional model training.

Cloudera and VAST Data take aim at GPU starvation with joint AI factory stack. by Harold Fritts on July 15, 2026 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. Engage with StorageReview Harold Fritts. I have been in the tech industry since IBM created Selectric. My background, though, is writing. So I decided to get out of the pre-sales biz and return to my roots, doing a bit of writing but still being involved in technology.