Browse by Chip Category

Choosing the correct semiconductor category is the most important step in any AI infrastructure project, because the accelerator, server CPU, and memory configuration you select directly determines training throughput, inference latency, and overall total cost of ownership. Our catalog is organized into ten focused categories spanning data center GPUs from NVIDIA, AMD, and Intel, hyperscaler ASICs such as Google TPU and Amazon Trainium, server CPUs, AI server platforms, networking interconnects, HBM memory, and enterprise storage.

For large language model training and fine-tuning workloads, data center GPU accelerators with high-bandwidth HBM memory and NVLink-scale interconnect are typically the right starting point, whereas production inference deployments often favor lower-power inference-optimized accelerators that maximize requests per second while minimizing power draw per rack. HPC simulation and scientific computing, by contrast, benefit from generalized compute platforms where server CPUs paired with capable accelerators deliver balanced floating-point performance.

If you are unsure which category matches your performance, compliance, or budget requirements, our certified engineering team provides free workload consultation and can recommend the exact architecture, model, and quantity for your use case, so you avoid over-provisioning hardware and overspending on compute you will never utilize.