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AMD’s upcoming beast the Instinct MI200 CDNA2 MCM GPU offers a 4x increase in FP16 compute compared to the existing MI100 GPU

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The first-ever GPU for the HPC segment to feature an MCM design based on the CDNA2 architecture, the MI200 from AMD is on the verge of being launched and we are getting reports that the GPU will offer some insane performance numbers compared to the existing Instinct MI100 GPU with a 4x increase in FP16 compute.

According to sources, the Instinct MI200 lineup will include two variants, which will be a standard MI250, and a MI250X. According to the details, the MI250X will get 110 CUs per die (220 CUs in total), 128 GB HBM2e memory, a 500W TDP and will be based on 7nm.

the tweets above suggest that AMD’s Instinct MI200 will rock a clock speed of up to 1.7 GHz which is a 13% increase over the Instinct MI100. The new MCM GPU will also be sporting twice the number of stream processors at 14,080 cores which will be packed within 220 Compute Units.

According to previous reports from HPCWire, AMD’s Instinct MI200 will power three top-tier supercomputers which include the United States’ exascale Frontier system; the European Union’s pre-exascale LUMI system; and Australia’s petascale Setonix system.

Performance-wise, AMD’s Instinct MI200 HPC Accelerator comes with almost 50 TFLOPs (47.9) TFLOPs of FP64 & FP32 compute horsepower.

AMD Radeon Instinct Accelerators 2020

Accelerator NameAMD Instinct MI300AMD Instinct MI200AMD Instinct MI100AMD Radeon Instinct MI60AMD Radeon Instinct MI50AMD Radeon Instinct MI25AMD Radeon Instinct MI8AMD Radeon Instinct MI6
GPU ArchitectureTBA (CDNA 3)Aldebaran (CDNA 2)Arcturus (CDNA 1)Vega 20Vega 20Vega 10Fiji XTPolaris 10
GPU Process NodeAdvanced Process NodeAdvanced Process Node7nm FinFET7nm FinFET7nm FinFET14nm FinFET28nm14nm FinFET
GPU Dies4 (MCM)?2 (MCM)1 (Monolithic)1 (Monolithic)1 (Monolithic)1 (Monolithic)1 (Monolithic)1 (Monolithic)
GPU Cores28,160?14,080?768040963840409640962304
GPU Clock SpeedTBA~1700 MHz~1500 MHz1800 MHz1725 MHz1500 MHz1000 MHz1237 MHz
FP16 ComputeTBA383 TOPs185 TFLOPs29.5 TFLOPs26.5 TFLOPs24.6 TFLOPs8.2 TFLOPs5.7 TFLOPs
FP32 ComputeTBA95.8 TFLOPs23.1 TFLOPs14.7 TFLOPs13.3 TFLOPs12.3 TFLOPs8.2 TFLOPs5.7 TFLOPs
FP64 ComputeTBA47.9 TFLOPs11.5 TFLOPs7.4 TFLOPs6.6 TFLOPs768 GFLOPs512 GFLOPs384 GFLOPs
VRAMTBA64/128 GB HBM2e?32 GB HBM232 GB HBM216 GB HBM216 GB HBM24 GB HBM116 GB GDDR5
Memory ClockTBATBA1200 MHz1000 MHz1000 MHz945 MHz500 MHz1750 MHz
Memory BusTBA8192-bit4096-bit bus4096-bit bus4096-bit bus2048-bit bus4096-bit bus256-bit bus
Memory BandwidthTBA~2 TB/s?1.23 TB/s1 TB/s1 TB/s484 GB/s512 GB/s224 GB/s
Form FactorTBADual Slot, Full Length / OAMDual Slot, Full LengthDual Slot, Full LengthDual Slot, Full LengthDual Slot, Full LengthDual Slot, Half LengthSingle Slot, Full Length
CoolingTBAPassive CoolingPassive CoolingPassive CoolingPassive CoolingPassive CoolingPassive CoolingPassive Cooling
TDPTBATBA300W300W300W300W175W150W

source

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Avatar of Nivedita Bangari
Nivedita Bangari
I am a software engineer by profession and technology is my love, learning and playing with new technologies is my passion.

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