As an NVIDIA Elite Partner, Exxact Corporation works closely with the NVIDIA team to ensure seamless factory development and support. We pride ourselves on providing value-added service standards unmatched by our competitors.
EDU Discounts Available
Purchase a qualified NVIDIA powered workstation or server from Exxact and gain exclusive EDU discounts.
Wide NVIDIA Platform Selection
Exxact offers a wide selection of workstation and server platforms to meet the individual compute needs of each customers unique use case.
Standard 3 Year Warranty
Have peace of mind, focus on what matters most, knowing your system is backed by a 3 year warranty and support.
Find the Right Fit for Your Needs
The Most Powerful End-to-End AI and HPC Data Center Platforms from Exxact
SXM4 Based
- 2x AMD EPYC 7002 CPUs
- 4x NVIDIA Tesla A100 SXM4-40GB + NVLink
- Up to 4x Mellanox InfiniBand HDR 200Gbps Cards
- Up to 8TB DDR4 Memory
- Up to 60TB NVMe Storage
- 2x AMD EPYC 7002 CPUs
- 8x NVIDIA Tesla A100 SXM4-40GB + NVSwitch
- Up to 9x Mellanox InfiniBand HDR 200Gbps Cards
- Up to 4TB DDR4 Memory
- Up to 96TB NVMe Storage
- 2x Intel Xeon Scalable Family
- 8x NVIDIA Tesla A100 SXM4-40GB + NVSwitch
- Up to 9x Mellanox InfiniBand HDR 200Gbps Cards
- Up to 4TB DDR4 Memory
- Up to 96TB NVMe Storage
PCIe Based
- 1x AMD EPYC 7002 CPUs
- Up to 4x NVIDIA A100, or A40 PCIe GPUs
- Up to 1TB DDR4 Memory
- Up to 60TB NVMe Storage
- 2x AMD EPYC 7002 CPUs
- Up to 8x NVIDIA A100, or A40 PCIe GPUs
- Up to 4TB DDR4 Memory
- Up to 96TB NVMe Storage
- 2x Intel Xeon CPUs
- Up to 8x NVIDIA A100, or A40 PCIe GPUs
- Up to 8TB DDR4 Memory
- Up to 96TB NVMe Storage
Compare Ampere’s GPU Variants
 | Model | Memory | Mem. Bandwidth | CUDA Cores | Tensor Cores | FP32 TFLOPs | FP64 TFLOPs | TP32 TFLOPs | Explore |
|---|---|---|---|---|---|---|---|---|---|
 | Ampere A100 SXM4 | 40GB HBM2e | 1.555 TB/s | 6912 | 432 | 19.5 | 9.7 | 156/312* | - |
 | Ampere A100 PCIe | 40GB HBM2e | 1.555 TB/s | 6912 | 432 | 19.5 | 9.7 | 156/312* | Specs |
What Form Factor is Right for Me? | Deep Learning Training | Deep Learning Inference | HPC and AI | Enterprise Acceleration |
|---|---|---|---|---|
SMX4 | 8-16 GPUs | 1 GPU w/ MIG | 4 GPUs with MIG for supercomputing centers | 1-4 GPUs with MIG for compute intensive multi GPU workloads |
PCIe | 4-8 GPUs | 1 GPU w/ MIG | 1-4 GPUs with MIG for higher ed and research | 1-4 GPUs with MIG for compute intensive single GPU workloads |
High-Performance Computing with NVIDIA Tesla A100
To unlock next-generation discoveries, scientists look to simulations to better understand complex molecules for drug discovery, physics for potential new sources of energy, and atmospheric data to better predict and prepare for extreme weather patterns.
A100 introduces double-precision Tensor Cores, providing the biggest milestone since the introduction of double-precision computing in GPUs for HPC. This enables researchers to reduce a 10-hour, double-precision simulation running on NVIDIA V100 Tensor Core GPUs to just four hours on A100. HPC applications can also leverage TF32 precision in A100’s Tensor Cores to achieve up to 10x higher throughput for single-precision dense matrix multiply operations.
Geometric mean of application speedups vs. P100: benchmark application: Amber [PME-Cellulose_NVE], Chroma [szscl21_24_128], GROMACS [ADH Dodec], MILC [Apex Medium], NAMD [stmv_nve_cuda], Pytorch [BERT Large Fine Tuner], Quantum Espresso [AUSURF112-jR]; Random Forest FP32 [make_blobs (160000 x 64 : 10)], TensorFlow [ResNet-50], VASP 6 [Si Huge], | GPU node with dual-socket CPUs with 4x NVIDIA P100, V100, or A100 GPUs.
Ampere A100 Accelerates Deep Learning Training and Inference
BERT pre training throughput using Pytorch, including (2/3) Phase 1 and (1/3) Phase 2 | Phase 1 Seq Len = 128, Phase 2 Seq Len = 512; V100: NVIDIA DGX-1™ server with 8x V100 using FP32 precision; A100: DGX A100 Server with 8x A100 using TF32 precision.
BERT Large Inference | NVIDIA T4 Tensor Core GPU: NVIDIA TensorRT (TRT) 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 7 MIG instances of 1g.5gb: pre-production TRT, batch size = 94, precision = INT8 with sparsity.
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