900-21001-0020-100 - Nvidia A100 Tensor Core 80GB HBM2e PCIe 4.0 GPU

Nvidia 900-21001-0020-100 A100 Tensor Core 80GB HBM2e 5120-Bit PCI Express 4.0 x16 1x 8-Pin Dual-Slot Full Height GPU Accelerator

The NVIDIA® A100 80GB PCIe GPU is engineered to accelerate the most demanding AI, high-performance computing (HPC), and data analytics workloads in modern data centers. Built on the powerful NVIDIA Ampere GA100 architecture, this GPU delivers industry-leading performance, memory bandwidth, and flexibility for data-intensive applications.

• Massive Memory Capacity : With 80GB of high-bandwidth memory, the A100 PCIe GPU supports the most complex models and largest datasets, enabling faster results and improved throughput across AI and HPC workloads.
• Advanced Compute Support : The A100 supports a wide range of math precisions including FP64 (double precision) , FP32 (single precision) , FP16 (half precision) , and INT8 (integer) , making it ideal for diverse workloads ranging from scientific computing to AI training and inference.
• Unmatched Memory Bandwidth : Offering up to 1.94 TB/s of memory bandwidth , the A100 80GB PCIe sets a new standard for data transfer performance, significantly reducing bottlenecks in large-scale computing tasks.
• High-Efficiency Cooling Design : The dual-slot, 10.5-inch PCIe Gen4 card features a passive heat sink , optimized for server airflow environments. It operates with a thermal design power (TDP) of 300W , ensuring peak performance in high-demand scenarios.
• Multi-Instance GPU (MIG) Technology : The A100 can be partitioned into up to seven isolated GPU instances , allowing multiple users or tasks to run concurrently on a single card. This feature boosts GPU utilization and offers true elasticity for dynamic workloads in cloud-native and virtualized data centers.
• Scalable Performance with NVLink : Using three NVIDIA® NVLink® bridges , multiple A100 80GB PCIe GPUs can be interconnected to achieve 600 GB/s total bandwidth , which is 10x faster than PCIe Gen4 alone. This enables massive scalability and accelerated performance for multi-GPU workloads.