Qualcomm Centriq 2400 Arm-Based Server Processor Now Shipping

Posted by at 10:13 am on November 9, 2017

At an event in San Jose on Wednesday, Qualcomm and partners officially announced that its Centriq 2400 server processor based on the Arm-architecture was shipping to commercial clients. This launch is of note as it becomes the highest-profile and most partner-lauded Arm-based server CPU and platform to be released.

The Centriq is built specifically for enterprise cloud workloads with an emphasis on high core count and high throughput and will compete against Intel’s Xeon Scalable and AMD’s new EPYC platforms.

Specifications and capabilities of the Centriq 2400

 

Centriq 2460 Centriq 2452 Centriq 2434
Architecture ARMv8 (64-bit)
Core: Falkor
ARMv8 (64-bit)
Core: Falkor
ARMv8 (64-bit)
Core: Falkor
Process Tech 10nm (Samsung) 10nm (Samsung) 10nm (Samsung)
Socket ? ? ?
Cores/Threads 48/48 46/46 40/40
Base Clock 2.2 GHz 2.2 GHz 2.3 GHz
Max Clock 2.6 GHz 2.6 GHz 2.5 GHz
Memory Tech DDR4 DDR4 DDR4
Memory Speeds 2667 MHz
128 GB/s
2667 MHz
128 GB/s
2667 MHz
128 GB/s
Cache 24MB L2, split
60MB L3
23MB L2, split
57.5MB L3
20MB L2, split
50MB L3
PCIe 32 lanes PCIe 3.0 32 lanes PCIe 3.0 32 lanes PCIe 3.0
Graphics N/A N/A N/A
TDP 120W 120W 120W
MSRP $1995 $1383 $888

Built on 18 billion transistors a die area of just 398mm2, the SoC holds 48 high-performance 64-bit cores running at frequencies as high as 2.6 GHz. (Interestingly, this appears to be about the same peak clock rate of all the Snapdragon processor cores we have seen on consumer products.) The cores are interconnected by a bi-directional ring bus that is reminiscent of the integration Intel used on its Core processor family up until Skylake-SP was brought to market. The bus supports 250 GB/s of aggregate bandwidth and Qualcomm claims that this will alleviate any concern over congestion bottlenecks, even with the CPU cores under full load.

Qualcomm states that its parts are designed for “highly threaded cloud native applications that are developed as micro-services and deployed for scale-out.

Here are some of the workloads it does target:

  • Web front end with HipHop Virtual Machine
  • NoSQL databases including MongoDB, Varnish, Scylladb
  • Cloud orchestration and automation including Kubernetes, Docker, metal-as-a-service
  • Data analytics including Apache Spark
  • Deep learning inference
  • Network function virtualization
  • Video and image processing acceleration
  • Multi-core electronic design automation
  • High throughput compute bioinformatics
  • Neural class networks
  • OpenStack Platform
  • Scaleout Server SAN with NVMe
  • Server-based network offload

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