TESLA A100│A100 80GB
STRUCTURAL SPARSITY
AI networks are big, having millions to billions of parameters. Not all of these parameters are needed for accurate predictions, and some can be converted to zeros to make the models “sparse” without compromising accuracy. Tensor Cores in A100 can provide up to 2X higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also improve the performance of model training.
MULTI-INSTANCE GPU (MIG)
An A100 GPU can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high-bandwidth memory, cache, and compute cores. MIG gives developers access to breakthrough acceleration for all their applications, and IT administrators can offer rightsized GPU acceleration for every job, optimizing utilization and expanding access to every user and application.
HD Gallery
- DESCRIPTION
- SPECIFICATION
- BIOS / DRIVERS UPDATE
- REQUIREMENT
►NVIDIA Ampere Architecture
►HBM2 Memory
►Third-Generation Tensor Cores
►Multi-Instance GPU (MIG)
►Structural Sparsity
►Next-Generation NVLINK
GPU Architecture |
NVIDIA Ampere |
Peak FP64 |
9.7 TF |
Peak FP64 Tensor Core |
19.5 TF |
Peak FP32 |
19.5 TF |
Peak TF32 Tensor Core |
156 TF | 312 TF* |
Peak BFLOAT16 Tensor Core |
312 TF | 624 TF* |
Peak FP16 Tensor Core |
312 TF | 624 TF* |
Peak INT8 Tensor Core |
624 TOPS | 1,248 TOPS* |
Peak INT4 Tensor Core |
1,248 TOPS | 2,496 TOPS* |
GPU Memory |
40 GB│80GB |
GPU Memory Bandwidth |
1,555 GB/s |
Interconnect |
NVIDIA NVLink 600 GB/s** |
Multi-instance GPUs |
Various instance sizes with up to 7MIGs @5GB |
Form Factor |
PCIe |
Max TDP Power |
250W |
Delivered Performance of Top Apps |
90 |