Supermicro 1-300-100-009 GPU Node Sled for Hyper Server Chassis

The Supermicro 1-300-100-009 GPU Node Sled is a purpose-built compute sled designed for integration within Supermicro's Hyper Server chassis platform, enabling high-density GPU-accelerated workloads in enterprise datacenter environments. The sled architecture supports the installation of discrete GPU accelerators alongside host CPU and memory resources, providing a modular and scalable approach to deploying parallel compute capacity within a shared chassis infrastructure. This design allows organizations to maximize rack utilization while maintaining the flexibility to configure GPU resources independently of other node types housed in the same enclosure.

Built to address the demanding requirements of AI inference, deep learning training, and high-performance computing, the GPU Node Sled integrates within Supermicro's broader Hyper platform ecosystem, which is characterized by shared power, cooling, and management backplane resources across multiple sleds. The sled form factor facilitates rapid node-level serviceability, allowing GPU compute nodes to be inserted or removed without disrupting adjacent sleds or requiring full chassis downtime. This modularity is particularly valuable in large-scale deployments where operational continuity and hardware lifecycle management are critical considerations.

The 1-300-100-009 is positioned as a premium infrastructure component for organizations building out GPU-dense environments for artificial intelligence, machine learning, data analytics, scientific simulation, and virtualized GPU workloads. Its compatibility with the Supermicro Hyper Server chassis family ensures alignment with enterprise-grade management tooling, including IPMI-based out-of-band management and integration with Supermicro's SuperBlade and Hyper management frameworks, supporting the full operational requirements of modern datacenter and cloud service provider environments across UAE, GCC, EMEA, and APAC regions.

Ideal for

• Large-scale AI and deep learning model training requiring high-density GPU compute nodes within a shared chassis infrastructure
• Enterprise inference serving deployments where modular GPU sled replacement enables rapid capacity scaling without chassis-level disruption
• High-performance computing clusters for scientific simulation, computational fluid dynamics, and molecular modeling workloads
• GPU-accelerated data analytics and business intelligence pipelines processing large structured and unstructured datasets
• Virtualized GPU environments supporting multiple concurrent users or virtual machines through GPU partitioning technologies
• Cloud service provider and colocation datacenter buildouts demanding maximized GPU density per rack unit with independent node serviceability

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.