A100 also adds Compute Data Compression to deliver up to an additional 4x improvement in DRAM bandwidth and L2 bandwidth, and up to 2x improvement in L2 capacity. To optimize capacity utilization, the NVIDIA Ampere architecture provides L2 cache residency controls for you to manage data to keep or evict from the cache. With a new partitioned crossbar structure, the A100 L2 cache provides 2.3x the L2 cache read bandwidth of V100. In addition, the A100 GPU has significantly more on-chip memory including a 40 MB Level 2 (L2) cache-nearly 7x larger than V100-to maximize compute performance. To feed its massive computational throughput, the NVIDIA A100 GPU has 40 GB of high-speed HBM2 memory with a class-leading 1.6 TB/sec of memory bandwidth – a 73% increase compared to Tesla V100. BF16 Tensor Core instructions at the same throughput as FP16.IEEE-compliant FP64 Tensor Core instructions for HPC.TF32 Tensor Core instructions that accelerate processing of FP32 data.The A100 third-generation Tensor Cores enhance operand sharing and improve efficiency, and add powerful new data types, including the following: The new streaming multiprocessor (SM) in the NVIDIA Ampere architecture-based A100 Tensor Core GPU significantly increases performance, builds upon features introduced in both the Volta and Turing SM architectures, and adds many new capabilities. Please note: a fully enabled GPU thus would have 8192 Cuda Cores and 48GB of HBM2 memory Tesla A100 features 40GB of HBM2e memory. Sized 826mm2 the GPU has 108 Streaming Multiprocessors x 64 Shader processors. The GPU has a 7nm Ampere GA100 GPU with 6912 shader processors and 432 Tensor cores. For HPC, the A100 Tensor Core includes new IEEE-compliant FP64 processing that delivers 2.5x the FP64 performance of V100. Tensor Core acceleration of INT8, INT4, and binary round out support for DL inferencing, with A100 sparse INT8 running 20x faster than V100 INT8. New Bfloat16 (BF16)/FP32 mixed-precision Tensor Core operations run at the same rate as FP16/FP32 mixed-precision. For FP16/FP32 mixed-precision DL, the A100 Tensor Core delivers 2.5x the performance of V100, increasing to 5x with sparsity. New TensorFloat-32 (TF32) Tensor Core operations in A100 provide an easy path to accelerate FP32 input/output data in DL frameworks and HPC, running 10x faster than V100 FP32 FMA operations or 20x faster with sparsity. Given the total memory bandwidth of 1550 GB/s, that is a 5120-bit memory bus.Ī100 adds a powerful new third-generation Tensor Core that boosts throughput over V100 while adding comprehensive support for DL and HPC data types, together with a new Sparsity feature that delivers a further doubling of throughput. You can see on the photos that there are six HBM2 stacks which together would account for a total of 40 gigabytes of video memory. The same device used for high-performance scientific computing can beat Volta’s performance by 2.5x (for double precision, 64-bit). Each SM of the A100 comes with 64 FP32 cores and 32 FP64 cores. The A100 video card uses PCI Express 4.0 and Nvidia's proprietary NVLink interface for super-fast mutual communication, reaching a top speed of 600 GB/s. Nvidia claims the A100 has 20x the performance of the equivalent Volta device for both AI training (single precision, 32-bit floating point numbers) and AI inference (8-bit integer numbers). Powered by 54 billion transistors, it’s the world’s largest 7nm chip, according to Nvidia, delivering more than one Peta-operations per second. The first chip built on Ampere, the A100, has some pretty impressive vital statistics. The first product with A100 is the DGX A100. The first ampere GPU A100 is said to offer 20 times more power than Volta in this scenario. The successor to Volta is aimed at use in the data center for AI training and eep learning. Nvidia has revealed initial details about the new GPU architecture Ampere.
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