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Cisco

Cisco UCSC-885A-M8-M353 | Rack server | 8x MI350X GPUs | 2x ConnectX-7 | 23TB RAM

SKU:UCSC-885A-M8-M353

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Description

8‑GPU AI compute node tailored for advanced training and large‑context inference, integrating MI350X accelerators with high‑speed ConnectX‑7 networking and a 23TB memory build. Ideal as a scale-out building block for modern AI clusters.

Features

- Dense 8-GPU configuration for state-of-the-art AI performance
- High-memory 23TB configuration for large-model and vector workloads
- ConnectX-7 fabric readiness for modern AI networking topologies
- Designed for reliability and 24/7 enterprise operation
- Flexible expansion for NICs and storage to fit cluster designs

Warranty

All products sold by XS Network Tech include a 12-month warranty on both new and used items. Our in-house technical team thoroughly tests used hardware prior to sale to ensure enterprise-grade reliability.

All technical data should be verified on the manufacturer data sheets.

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FAQs

Technical Specifications

FAQs

Q: Is this node ready for use in an AI cluster out of the box?
A: It ships with 8x MI350X GPUs, 23TB memory, and ConnectX-7 networking, making it suitable as a drop-in node; software stack configuration is customer-specific.

Q: Can I add additional NICs or storage?
A: Yes, expansion options are available; confirm available slots and supported adapters for your exact configuration.

Q: Does it support containerized AI stacks?
A: Yes, it is suitable for Kubernetes- or Slurm-based AI/ML workflows; verify with your preferred distribution.

Q: Are power supplies redundant?
A: The platform supports redundant PSUs; exact PSU configuration depends on the specific bundle.

Q: What interconnect speeds are supported by ConnectX-7?
A: ConnectX-7 adapters support leading-edge Ethernet/InfiniBand link rates; refer to the installed card specification for exact speed.

Technical Specifications

- Series: UCS C885A M8 rack server
- Accelerators: 8x MI350X GPUs
- Networking: 2x ConnectX-7 adapters
- System memory (installed): 23TB
- Workloads: LLM training, multi-modal inference, HPC, data science
- Cluster role: scale-out AI node with high-bandwidth fabric
- Chassis: enterprise rack-mount design

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