Cisco Cisco UCS C460 M4 Rack Server Ficha De Dados

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Data Sheet 
Cisco UCS C460 M4 Rack Server 
Product Overview 
The Cisco UCS
®
 C460 M4 Rack Server (Figure 1) provides the performance and reliability to run mission-critical 
applications and virtualized workloads that require intensive computation processing and very high memory 
capacity. Applications that are memory-bound (for example, large-scale virtualization, massive database 
applications, and server consolidation) will benefit from the increased performance and memory footprint of the 
Cisco UCS C460 M4. 
The Cisco UCS C460 M4 is a four-rack-unit (4RU) rack server supporting the Intel
®
 Xeon
®
 E7-4800/8800 v2, v3, 
and v4 processor families. Product highlights include: 
● 
Up to 6 terabytes (TB) of double-data-rate 3 (DDR3) memory or double-data-rate 4 (DDR4) memory in 96 
DIMM slots 
● 
Up to 12 Small Form Factor (SFF) hot-pluggable SAS, SATA, or SSD disk drives with support for 2 PCle 
SSD 
● 
Abundant I/O capability with 10 PCI Express (PCIe) Generation 3 (Gen 3) slots supporting the Cisco UCS 
virtual interface cards (VICs). An internal slot is reserved for a hard-disk drive array controller card 
● 
Two Gigabit Ethernet LAN-on-motherboard (LOM) ports, two 10-Gigabit Ethernet ports, and a dedicated 
out-of-band (OOB) management port that provides additional networking options 
Figure 1.    Cisco UCS C460 M4 Rack Server 
 
Applications 
The Cisco UCS C460 M4 Rack Server offers industry-leading performance and advanced reliability well suited for 
the most demanding enterprise and mission-critical workloads, large-scale virtualization, and database 
applications. Whether the Cisco UCS C460 M4 is used as a standalone system or in a Cisco Unified Computing 
System
 
(Cisco UCS) deployment, customers gain the benefits of the server’s high-capacity memory when very 
large memory footprints such as the following are required: 
● 
SAP workloads 
● 
Database applications and data warehousing 
● 
Large virtualized environments 
● 
Real-time financial applications 
● 
Java-based workloads 
● 
Server consolidation