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The following table lists the Dell systems used for the inference benchmark:
Platform | PowerEdge XE8545 4xA100 TensorRT | PowerEdge XE8545 4xA100 TensorRT, MaxQ |
MLPerf system ID | XE8545_A100_SXM_80GBx4_TRT | XE8545_A100_SXM_80GBx4_TRT_MaxQ |
Operating system | Ubuntu 20.04.3 | |
CPU | AMD EPYC 7763 | |
Memory | 1 TB | |
GPU | NVIDIA A100-SXM-80GB CTS | |
GPU form factor | SXM | |
GPU count | 4 | |
Software Stack | TensorRT 8.4.2 | |
System suite type | Data center |
Platform | PowerEdge R750xa 4xA100 TensorRT | PowerEdge XR12 1xA2 TensorRT |
MLPerf system ID | R750xa_A100_PCIE_80GBx4_TRT | XR12_A2x1_TRT_MaxQ |
Operating system | CentOS 8.2 | |
CPU | Intel Xeon Gold 6338 CPU @ 2.00 GHz | Intel Xeon Gold 6312U CPU @ 2.40 GHz |
Memory | 512 GB | 256 GB |
GPU | NVIDIA A100-PCIe-80GB | NVIDIA A2 |
GPU form factor | PCIE | |
GPU count | 4 | 1 |
Software Stack | TensorRT 8.4.2 | |
System suite type | Data center | Data center, Edge |
This round of submission marks our sixth submission to MLPerf Inference v2.1. Our submission had four different configurations that include NVIDIA-based accelerators. They were inclusive of data center and edge suites. For the benchmarks, we used Dell PowerEdge XR12, R750xa, and XE8545 servers with NVIDIA A2, A100 with PCIE, and A100 with SXM accelerators. We ran the configurations on the TensorRT backend and some configurations set to MaxQ. MaxQ refers to measuring both the performance and power consumed by the system.
This round demonstrates higher performance on our data center systems and performance/watt on our MaxQ, data center, and edge systems. Overall, Dell servers delivered outstanding performance across different workloads and deployment scenarios, making them suitable for inference workloads.
NVIDIA accelerator-based system IDs in our submissions follow this syntax:
<Dell server name>_<NVIDIA Accelerator name and config>x<Accelerator count>_<inference backend>_<MaxQ if power submission>
Examples: