NVIDIA - GPU computing processor
A100 Tensor Core | 80 GB HBM2 | PCIe 4.0 x16 | fanless | for ThinkAgile VX3530-G Appliance; VX7531 Certified Node; ThinkSystem SR650 V2; SR665
Main Specification
| Product Description | NVIDIA - GPU computing processor - A100 Tensor Core - 80 GB |
|---|---|
| Device Type | GPU computing processor |
| Bus Type | PCI Express 4.0 x16 |
| Graphics Engine | NVIDIA A100 Tensor Core |
| Memory | 80 GB HBM2 |
| CUDA Cores | 6912 |
| API Supported | OpenCL, DirectCompute, OpenACC |
| Fanless | Yes |
| Designed For | ThinkAgile VX3530-G Appliance 7Z63; VX7531 Certified Node 7Z63; ThinkSystem SR650 V2 7D15; SR665 7D2V, 7D2W |
General
| Device Type | GPU computing processor - fanless |
|---|---|
| Bus Type | PCI Express 4.0 x16 |
| Graphics Engine | NVIDIA A100 Tensor Core |
| CUDA Cores | 6912 |
| API Supported | OpenCL, DirectCompute, OpenACC |
| Features | Nvidia CUDA technology, Error Correcting Codes (ECC) Memory, dual slot width, NVIDIA Tensor Core, NVIDIA Ampere GPU technology, 9.7 Tflops peak double-precision floating point performance, 19.5 Tflops peak single-precision floating point performance, 312 Tflops peak half-precision floating point performance, full-height full-length (FHFL), Multi-Instance GPU (MIG) technology, 432 third-generation Tensor Cores per GPU |
Memory
| Size | 80 GB |
|---|---|
| Technology | HBM2 |
Miscellaneous
| Power Consumption Operational | 300 watt |
|---|
Compatibility Information
| Designed For | Lenovo ThinkAgile VX3530-G Appliance 7Z63 Lenovo ThinkAgile VX7531 Certified Node 7Z63 Lenovo ThinkSystem SR650 V2 7D15 Lenovo ThinkSystem SR665 7D2V, 7D2W |
|---|
Product features
Deep learning training
NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20x higher performance over the NVIDIA Volta with zero code changes and an additional 2x boost with automatic mixed precision and FP16. A training workload like BERT can be solved at scale in under a minute by 2,048 A100 GPUs, a world record for time to solution.Deep learning inference
A100 introduces groundbreaking features to optimize inference workloads. It accelerates a full range of precision, from FP32 to INT4. Multi-Instance GPU (MIG) technology lets multiple networks operate simultaneously on a single A100 for optimal utilization of compute resources. And structural sparsity support delivers up to 2x more performance on top of A100's other inference performance gains.High-performance data analytics
Data scientists need to be able to analyze, visualize, and turn massive datasets into insights. But scale-out solutions are often bogged down by datasets scattered across multiple servers. Accelerated servers with A100 provide the needed compute power - along with massive memory, over 2 TB/sec of memory bandwidth, and scalability with NVIDIA NVLink and NVSwitch, - to tackle these workloads. Combined with InfiniBand, NVIDIA Magnum IO and the RAPIDS suite of open-source libraries, including the RAPIDS Accelerator for Apache Spark for GPU-accelerated data analytics, the NVIDIA data center platform accelerates these huge workloads at unprecedented levels of performance and efficiency.Enterprise-ready utilization
A100 with MIG maximizes the utilization of GPU-accelerated infrastructure. With MIG, an A100 GPU can be partitioned into as many as seven independent instances, giving multiple users access to GPU acceleration. MIG works with Kubernetes, containers, and hypervisor-based server virtualization. MIG lets infrastructure managers offer a right-sized GPU with guaranteed quality of service (QoS) for every job, extending the reach of accelerated computing resources to every user.Key selling points
- Deep learning training
- Deep learning inference
- High-performance data analytics
- Enterprise-ready utilization
References
MPN: 4X67A76715
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04/06/2026 10:57:29