Description
The Cisco UCS C885A M8 CPU Sled (C885A-M8-CC-SLD02=) is a high-performance compute module engineered for Cisco’s UCS C885A M8 dense GPU server platform. Powered by dual AMD EPYC 9575F processors and equipped with 96GB of ultra-fast DDR5-6400 memory, this CPU sled delivers exceptional throughput for AI training, HPC workloads, and large-scale data processing.
Designed to support advanced east-west and north-south networking, the sled integrates BlueField-3 B3140H and B3220 SuperNIC capabilities for optimized GPU communication and low-latency cluster scaling. Built for enterprise and hyperscale environments, it provides the compute foundation required for modern AI, machine learning, and accelerated computing deployments.
| Specification | Details |
|---|---|
| Product SKU | C885A-M8-CC-SLD02= |
| Product Type | CPU Sled |
| Compatible Platform | Cisco UCS C885A M8 Rack Server |
| Processor | AMD EPYC 9575F |
| CPU Architecture | 5th Gen AMD EPYC |
| Installed Memory | 96GB DDR5 |
| Memory Speed | DDR5-6400 MT/s |
| Networking | BlueField-3 B3140H SuperNIC |
| Additional Networking | B3220 SuperNIC |
| PCIe Generation | PCIe Gen 5 |
| Workload Focus | AI Training, HPC, GPU-Accelerated Compute |
| Form Factor | Modular CPU Sled |
| Management | Cisco UCS / Intersight Ready |
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Return Policy (Short Summary)
We offer a 30-day return window for most unopened, factory-sealed items.
Returns must be authorized before shipping back and must include all original packaging and accessories. Opened or used items are not returnable unless defective.
Some products—such as custom orders, special-order items, audio gear, software, and LED video walls—are non-returnable. Non-defective returns may be subject to a 20–30% restocking fee, and customers are responsible for return shipping costs.
For damaged or incorrect items, please contact us immediately.
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This product is rarely deployed alone. It’s typically part of a fully integrated AV system. AVENDOR designs, integrates, and supports end-to-end environments—from single rooms to enterprise-wide deployments.
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