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NVIDIA announces DGX Station A100 powered by Ampere A100 Tensor Core GPUs

NVIDIA announced its 2nd Generation DGX Station AI server. The server is based on companies latest Ampere A100 Tensor Core GPUs. The A100 is aimed at the fastest growing AI market and is designed to accelerate machine learning and data science performance.

Four A100 Tensor Core GPUs power the A100. The Tensor Core GPUs in A100 comes with 80 GB of HBM2e memory. It is twice the memory of the original A100. DGX Station has a total of 320 GB of total memory capacity. It supports the 3rd Gen NVLink support and offers 200 GB/s of bidirectional bandwidth.

The system, however, is powered by AMD EPYC Rome 64 Core CPU. It comes with PCIe Gen 4 support along with 512 GB of dedicated system memory. Not to mention the 1.92 TB NVME M.2 SSD storage and 7.68 TB NVME U.2 SSD storage.

The rest of specifications of the 2nd Generation DGX Station remains the same.

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NVIDIA Ampere GA100 GPU Specs:

NVIDIA Tesla Graphics CardTesla K40
(PCI-Express)
Tesla M40
(PCI-Express)
Tesla P100
(PCI-Express)
Tesla P100 (SXM2)Tesla V100 (SXM2)Tesla V100S (PCIe)NVIDIA A100 (SXM4)NVIDIA A100 (PCIe4)
GPUGK110 (Kepler)GM200 (Maxwell)GP100 (Pascal)GP100 (Pascal)GV100 (Volta)GV100 (Volta)GA100 (Ampere)GA100 (Ampere)
Process Node28nm28nm16nm16nm12nm12nm7nm7nm
Transistors7.1 Billion8 Billion15.3 Billion15.3 Billion21.1 Billion21.1 Billion54.2 Billion54.2 Billion
GPU Die Size551 mm2601 mm2610 mm2610 mm2815mm2815mm2826mm2826mm2
SMs152456568080108108
TPCs1524282840405454
FP32 CUDA Cores Per SM192128646464646464
FP64 CUDA Cores / SM644323232323232
FP32 CUDA Cores28803072358435845120512069126912
FP64 CUDA Cores96096179217922560256034563456
Tensor CoresN/AN/AN/AN/A640640432432
Texture Units240192224224320320432432
Boost Clock875 MHz1114 MHz1329MHz1480 MHz1530 MHz1601 MHz1410 MHz1410 MHz
TOPs (DNN/AI)N/AN/AN/AN/A125 TOPs130 TOPs1248 TOPs
2496 TOPs with Sparsity
1248 TOPs
2496 TOPs with Sparsity
FP16 ComputeN/AN/A18.7 TFLOPs21.2 TFLOPs30.4 TFLOPs32.8 TFLOPs312 TFLOPs
624 TFLOPs with Sparsity
312 TFLOPs
624 TFLOPs with Sparsity
FP32 Compute5.04 TFLOPs6.8 TFLOPs10.0 TFLOPs10.6 TFLOPs15.7 TFLOPs16.4 TFLOPs156 TFLOPs
(19.5 TFLOPs standard)
156 TFLOPs
(19.5 TFLOPs standard)
FP64 Compute1.68 TFLOPs0.2 TFLOPs4.7 TFLOPs5.30 TFLOPs7.80 TFLOPs8.2 TFLOPs19.5 TFLOPs
(9.7 TFLOPs standard)
19.5 TFLOPs
(9.7 TFLOPs standard)
Memory Interface384-bit GDDR5384-bit GDDR54096-bit HBM24096-bit HBM24096-bit HBM24096-bit HBM26144-bit HBM2e6144-bit HBM2e
Memory Size12 GB GDDR5 @ 288 GB/s24 GB GDDR5 @ 288 GB/s16 GB HBM2 @ 732 GB/s
12 GB HBM2 @ 549 GB/s
16 GB HBM2 @ 732 GB/s16 GB HBM2 @ 900 GB/s16 GB HBM2 @ 1134 GB/s40 GB HBM2 @ 1.6 TB/sUp To 80 GB HBM2 @ 1.6 TB/s
L2 Cache Size1536 KB3072 KB4096 KB4096 KB6144 KB6144 KB40960 KB40960 KB
TDP235W250W250W300W300W250W400W250W

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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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