NVIDIA A100 80GB PCIe Accelerator Launched

NVIDIA has announced that its launching today its brand new HGX A100 systems that incorporate the updated A100 PCIe GPU accelerators featuring twice the memory & faster bandwidth for HPC users.

NVIDIA Upgrades HGX A100 Systems With Flagship Ampere Based A100 HPC GPU Accelerators – 80 GB HBM2e Memory & 2 TB/s Bandwidth

The existing NVIDIA A100 HPC accelerator was introduced last year in June and it looks like the green team is planning to give it a major spec upgrade. The chip is based on NVIDIA’s largest Ampere GPU, the A100, which measures 826mm2 and houses an insane 54 billion transistors. NVIDIA gives its HPC accelerators a spec boost during mid-cycle which means that we will be hearing about the next-generation accelerators at GTC 2022.

NVIDIA Readies Ampere A100 PCIe GPU With 80 GB HBM2e Memory & Up To 2 TB/s Bandwidth

NVIDIA A100 Tensor Core GPUs deliver unprecedented HPC acceleration to solve complex AI, data analytics, model training and simulation challenges relevant to industrial HPC. A100 80GB PCIe GPUs increase GPU memory bandwidth 25 percent compared with the A100 40GB, to 2TB/s, and provide 80GB of HBM2e high-bandwidth memory.

The A100 80GB PCIe’s enormous memory capacity and high-memory bandwidth allow more data and larger neural networks to be held in memory, minimizing internode communication and energy consumption. Combined with faster memory bandwidth, it enables researchers to achieve higher throughput and faster results, maximizing the value of their IT investments.

A100 80GB PCIe is powered by the NVIDIA Ampere architecture, which features Multi-Instance GPU (MIG) technology to deliver acceleration for smaller workloads such as AI inference. MIG allows HPC systems to scale compute and memory down with guaranteed quality of service. In addition to PCIe, there are four- and eight-way NVIDIA HGX A100 configurations.

NVIDIA partner support for the A100 80GB PCIe includes Atos, Cisco, Dell Technologies, Fujitsu, H3C, HPE, Inspur, Lenovo, Penguin Computing, QCT and Supermicro. The HGX platform featuring A100-based GPUs interconnected via NVLink is also available via cloud services from Amazon Web Services, Microsoft Azure and Oracle Cloud Infrastructure.

In terms of specifications, the A100 PCIe GPU accelerator doesn’t change much in terms of core configuration. The GA100 GPU retains the specifications we got to see on the 250W variant with 6912 CUDA cores arranged in 108 SM units, 432 Tensor Cores, and 80 GB of HBM2e memory that delivers higher bandwidth of 2.0 TB/s compared to 1.55 TB/s on the 40 GB variant.

A featured image of the NVIDIA GA100 die.

The A100 SMX variant already comes with 80 GB memory but it doesn’t feature the faster HBM2e dies like this upcoming A100 PCIe variant. This is also the most amount of memory ever featured on a PCIe-based graphics card but don’t expect consumer graphics cards to feature such high capacities any time soon. What’s interesting is that the power rating remains unchanged which means that we are looking at higher density chips binned for high-performance use cases.

Specifications of the A100 PCIe 80 GB graphics card as listed over at NVIDIA’s webpage. (Image Credits: Videocardz)

The FP64 performance is still rated at 9.7/19.5 TFLOPs, FP32 performance is rated at 19.5 /156/312 TFLOPs (Sparsity), FP16 performance is rated at 312/624 TFLOPs (Sparsity) and the INT8 is rated at 624/1248 TOPs (Sparsity). NVIDIA is planning to release its latest HPC accelerator next week and we can also expect the pricing of over $20,000 US considering the 40 GB A100 variant sells for around $15,000 US.

In addition to these announcements, NVIDIA has also announced its new InfiniBand solution that provides configurations of up to 2048 points of NDR 400 Gb/s (or 4096 ports of NDR 200) with a total bi-directional throughput of 1.64 Pb/s. That alone is a 5x increase over the previous-gen and offers 32x higher AI accelerator.

NVIDIA Ampere GA100 GPU Based Tesla A100 Specs:

NVIDIA Tesla Graphics Card Tesla K40
Tesla M40
Tesla P100
Tesla P100 (SXM2) Tesla V100 (SXM2) Tesla V100S (PCIe) NVIDIA A100 (SXM4) NVIDIA A100 (PCIe4)
GPU GK110 (Kepler) GM200 (Maxwell) GP100 (Pascal) GP100 (Pascal) GV100 (Volta) GV100 (Volta) GA100 (Ampere) GA100 (Ampere)
Process Node 28nm 28nm 16nm 16nm 12nm 12nm 7nm 7nm
Transistors 7.1 Billion 8 Billion 15.3 Billion 15.3 Billion 21.1 Billion 21.1 Billion 54.2 Billion 54.2 Billion
GPU Die Size 551 mm2 601 mm2 610 mm2 610 mm2 815mm2 815mm2 826mm2 826mm2
SMs 15 24 56 56 80 80 108 108
TPCs 15 24 28 28 40 40 54 54
FP32 CUDA Cores Per SM 192 128 64 64 64 64 64 64
FP64 CUDA Cores / SM 64 4 32 32 32 32 32 32
FP32 CUDA Cores 2880 3072 3584 3584 5120 5120 6912 6912
FP64 CUDA Cores 960 96 1792 1792 2560 2560 3456 3456
Tensor Cores N/A N/A N/A N/A 640 640 432 432
Texture Units 240 192 224 224 320 320 432 432
Boost Clock 875 MHz 1114 MHz 1329MHz 1480 MHz 1530 MHz 1601 MHz 1410 MHz 1410 MHz
TOPs (DNN/AI) N/A N/A N/A N/A 125 TOPs 130 TOPs 1248 TOPs
2496 TOPs with Sparsity
1248 TOPs
2496 TOPs with Sparsity
FP16 Compute N/A N/A 18.7 TFLOPs 21.2 TFLOPs 30.4 TFLOPs 32.8 TFLOPs 312 TFLOPs
624 TFLOPs with Sparsity
312 TFLOPs
624 TFLOPs with Sparsity
FP32 Compute 5.04 TFLOPs 6.8 TFLOPs 10.0 TFLOPs 10.6 TFLOPs 15.7 TFLOPs 16.4 TFLOPs 156 TFLOPs
(19.5 TFLOPs standard)
156 TFLOPs
(19.5 TFLOPs standard)
FP64 Compute 1.68 TFLOPs 0.2 TFLOPs 4.7 TFLOPs 5.30 TFLOPs 7.80 TFLOPs 8.2 TFLOPs 19.5 TFLOPs
(9.7 TFLOPs standard)
19.5 TFLOPs
(9.7 TFLOPs standard)
Memory Interface 384-bit GDDR5 384-bit GDDR5 4096-bit HBM2 4096-bit HBM2 4096-bit HBM2 4096-bit HBM2 6144-bit HBM2e 6144-bit HBM2e
Memory Size 12 GB GDDR5 @ 288 GB/s 24 GB GDDR5 @ 288 GB/s 16 GB HBM2 @ 732 GB/s
12 GB HBM2 @ 549 GB/s
16 GB HBM2 @ 732 GB/s 16 GB HBM2 @ 900 GB/s 16 GB HBM2 @ 1134 GB/s Up To 40 GB HBM2 @ 1.6 TB/s
Up To 80 GB HBM2 @ 1.6 TB/s
Up To 40 GB HBM2 @ 1.6 TB/s
Up To 80 GB HBM2 @ 2.0 TB/s
L2 Cache Size 1536 KB 3072 KB 4096 KB 4096 KB 6144 KB 6144 KB 40960 KB 40960 KB
TDP 235W 250W 250W 300W 300W 250W 400W 250W

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