H100 Workload Benchmark
Test your workload on Silicon Mechanics’ new H100-powered GPU systems
Modern compute-intensive workloads in AI (Artificial Intelligence) training and inference all massively benefit from increased GPU performance. With each new generation of GPU, performance improves significantly. Of course, migrating to these new systems can be expensive, so you want to make sure the performance benefits of NVIDIA H100-powered systems are represented in your workload. To support these users, Silicon Mechanics is offering workload benchmarking sessions on our new NVIDIA H100-based GPU servers.
These systems pair NVIDIA AH00 GPUs with AMD EPYC 9004 CPUs –the world’s highest-performing x86 server CPU --to minimize bottlenecks between the compute and the accelerators.
Each system is pre-loaded with key AI and ML software tools such as:
- TensorFlow
- Python
- PIP
- CUDA
- cuDDN
- PyTorch
- Keras
- Jupyter
- Anaconda
- NGC Containers
- R
Ideal Workloads:
- Natural Language Processing
- Machine Learning
- Deep Learning
- Predictive Analytics
- Business Intelligence
- Image processing
- Object detection/recognition/classification
- Computer vision
Our platform offers the performance of leading GPU accelerated server appliances without the high total cost of ownership (TCO) of thousands of servers or the locked-in design of pre-configured GPU accelerated appliances.
Dual Socket AMD EPYC 9654, 96C/192T DP/UP 384M 360W SP5
24x 32GB DDR5-4800 2Rx8 LP (16Gb) ECC RDIMM
1x IntelD7-P5520 1.92TB NVMe PCIe4.0 X4 3DTLCU.2 15mm<2DWPD
2x IntelD7-P5520 3.84TB NVMe PCIe4.0 X4 3DTLCU.2 15mm<2DWPD
2x NVIDIA H100 80GB PCIe 5.0 x16 Passive Cooling
Expert Included
Our engineers are not only experts in traditional HPC and AI technologies, we also routinely build complex rack-scale solutions with today's newest innovations so that we can design and build the best solution for your unique needs.
Talk to an engineer and see how we can help solve your computing challenges today.