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New Memory Architecture for the Virtualized Data Center
The Cisco UCS B250 M1 Extended Memory Blade Server is designed to maximize performance and capacity for the most demanding virtualization and large dataset applications. The server is a full-width, two-socket blade server with increased throughput and more than double the amount of industry-standard memory compared to traditional two-socket x86 servers.
The UCS B250 M1 features include:
- Up to two Intel® Xeon® 5500 Series processors, which automatically and intelligently adjust server performance according to application needs, increasing performance when needed and achieving substantial energy savings when not.
- Up to 384 GB of DDR3 memory for demanding virtualization and large data-set applications. Alternatively, this technology offers a more cost-effective memory footprint for less-demanding workloads.
- Two optional Small Form Factor (SFF) Serial Attached SCSI (SAS) hard drives available in 73GB 15K RPM and 146GB 10K RPM versions with an LSI Logic 1064e controller and integrated RAID.
- Two dual-port mezzanine cards for up to 40 Gbps of I/O per blade. Mezzanine card options include either a Cisco UCS VIC M81KR Virtual Interface Card, a converged network adapter (Emulex or QLogic compatible), or a single 10GB Ethernet Adapter.
Cisco Extended Memory Technology Overview Video (3:29 min)
See how Cisco's patented technology increases performance and capacity for demanding virtualization and large-data-set workloads.
A Short Primer on Cisco Extended Memory (PDF - 2.6 MB)
Cisco Extended Memory technology provides more cost-effective large memory footprints for virtualized and non-virtualized workloads.
How It Works - Video Demo
Accelerate the delivery of new services simply, reliably, and securely through end-to-end provisioning and migration support for both virtualized and nonvirtualized systems.
Extended Memory Technology Overview (PDF - 2.6 MB)
Cisco Extended Memory Technology provides more than twice as much memory (384 GB) as traditional two-socket servers, increasing performance and capacity for demanding virtualization and large data-set workloads.
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