Lead Reconfigurable Memory Computing to the Feature

CloudCard

F12010

Process

Frequency

16nm

200MHz

Support for Computing Precision

INT8

INT16

FP16

FP32

Peak INT8 TOPS

1.638TOPS

Memory Capacity

2x DDR 4 16GB

bit width: 512bit

bandwidth: 154GB/s

Interface

PCIe Gen3 x 16

Compatible PCIe Gen 4 x 8

bandwidth: 16GB/s

Thermal Cooling Solution

passive

Form Factor

Double slot, full height 3/4 length

F11610E

16nm

1x DDR 4 16GB

bit width: 256bit

bandwidth: 102.4GB/s

PCIe Gen3 x 16

Compatible PCIe Gen 4 x 8

bandwidth: 16GB/s

162MHz

145TOPS

passive

INT8

INT16

FP16

FP32

G41210E

12nm

4x 32GB

bit width: 512bit

bandwidth: GDDR5:512GB/s
                  GDDR5X:704GB/s
                   GDDR6:1024GB/s

PCIe Gen4 x 16

bandwidth: 32GB/s

1.2GHz

>1000TOPS

passive

INT8

INT16

FP16

FP32

G40710E

7nm

PCIe Gen4 x 16

bandwidth: 32GB/s

1.6GHz

>4000TOPS

passive

INT8

INT16

FP16

FP32

Double slot, full height and full length

Double slot, full height and full length

Double slot, full height and full length

4x 32GB

bit width: 512bit

bandwidth: GDDR5:512GB/s
                  GDDR5X:704GB/s
                   GDDR6:1024GB/s

Product Parameters

Support to Various 
Application Scenarios

▪    Reconfigurable Computing-in-Memory technology for multi-operator support

▪    Adaptatibility to multiple models

▪    Compatibility with multiple frameworks to support various application scenarios

High Density Surge Computing Power

▪    Advanced Computing-in-Memory architecture with advanced process

▪    Multi-core & multi-chip for efficient and high-density AI computing power

Flexible and Highly Compatible Development Platform

▪    A development platform built on a universal AI platform

▪    Supports multiple mainstream deep learning frameworks

▪    Simplifies the development flow and easily ports existing algorithms

Ultra-Low Energy Cost on Operation

▪    Breaks the traditional power comsumption wall for data transformation, with ultra-high energy efficiency ratio to bring excellent deployment operation cost performance

Application Scenarios