VAST Data and AMD Deepen Partnership to Support AI Infrastructure
VAST Data, the AI Operating System company, is expanding its collaboration with AMD to help AI cloud providers and enterprises build and operate high-performance AI factories at scale.
According to the companies, the collaboration brings together the VAST AI Operating System with 6th Gen AMD EPYC CPUs and AMD Instinct GPUs to support scalable training, inference, and agentic AI workloads with improved efficiency, flexibility, and performance.
Crucial to this unified approach is VAST’s inherently versatile Disaggregated Shared Everything (DASE) architecture, which extends beyond standard storage to deliver robust multi-tenancy support for secure workload isolation, native multi-protocol access, and a unified global namespace that simplifies data access across distributed environments.
By streamlining critical data operations such as rapid model loading alongside VAST's core data platform values, the system allows AI clouds to run massive, concurrent workloads while maximizing hardware utilization, the vendors said.
VAST and AMD are expanding their collaboration to deliver an open and flexible approach to AI infrastructure that combines accelerated computing, intelligent data services and optimized inference software into a unified platform for AI clouds and enterprise AI deployments.
The collaboration includes:
- VAST has selected 6th Gen AMD EPYC processors, formerly codenamed “Venice,” to power the 6th-generation of CBox and 3rd-generation of EBox platforms to underpin the VAST AI OS. 6th Gen EPYC CPUs bring support for PCIe Gen-6 that enables 2X the I/O bandwidth generationally for improved file and object storage performance and helps improve CPU core performance by lowering latency for critical AI data services including database, data warehouse, and event streaming via VAST’s DataBase and DataEngine capabilities.
- An AI Infrastructure Reference Architecture developed by VAST, AMD, and DriveNets that features AMD Helios rack-scale AI infrastructure coupled with the VAST AI OS and DriveNets AI Fabric networking. These reference architectures document infrastructure support for model training, inference, reinforcement learning (RL), and KV cache workloads with sizing considerations and guidance for AI cloud providers and enterprises to simplify and accelerate high performance, highly available and efficient AI factory deployments.
- Expanded ecosystem collaboration with software innovators including TensorMesh and EmbeddedLLM to accelerate deployment of production-ready inference architectures optimized for agentic AI applications.
- The AMD Pensando Pollara 400 AI NIC provides the high-performance data path connecting AMD Instinct GPUs to the VAST AI OS. Using NFS over TCP and NFS over RDMA, this integration efficiently moves data from GPU memory to the NVMe SSD-based VAST storage cluster, enabling the KV-cache and storage access that large-scale inference and agentic AI workloads depend on.
- Proven deployments across leading AI cloud providers delivering AMD technology-powered AI services to customers around the world.
“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. The VAST AI Operating System was built for this transition, giving AI cloud providers and enterprises a more efficient, scalable and open foundation for training, inference and the next generation of agentic AI applications.
The VAST AI Operating System serves as the data and execution layer for these environments, enabling organizations to unify storage, database, streaming, and AI services within a single software platform, the company said.
By treating model management as a core data service, VAST virtually eliminates cold-start bottlenecks with near-instantaneous model loading that maximizes GPU utilization. Combined with AMD accelerated computing, customers can deploy scalable infrastructure capable of supporting training, inference, retrieval-augmented generation and agentic AI workloads across distributed environments, said the vendors.
For more information about this news, visit www.vastdata.com.