Redefining Possibilities with Next-Gen AI Solutions

Frequently Asked Questions

• What's the difference between AI inference and training hardware requirements?

  AI inference typically requires lower memory bandwidth and can run efficiently on single GPUs like RTX 4090 or L40S, while training demands high memory capacity and bandwidth, often requiring A100 or H100 GPUs with NVLink. Our consultation process analyzes your specific models and datasets to recommend the optimal configuration, ensuring you don’t overspend on training-grade hardware for inference workloads.

• How do I know if I need a GPU server or workstation?

  GPU workstations are ideal for development, prototyping, and small-scale training where researchers need dedicated local resources. GPU servers are designed for production deployments, 24/7 inference operations, or distributed training across multiple GPUs. We assess your workflow, team size, and deployment timeline to recommend the right platform—many clients benefit from a hybrid approach.

• Can you help me avoid overbuying GPU capacity?

  Absolutely. Our right-sizing methodology has saved clients an average of 35% on hardware costs. We analyze your actual workload requirements, growth projections, and performance targets to specify exactly what you need. This includes GPU selection, memory configuration, and even planning for future expansion without initial overinvestment.

• Do you only sell NVIDIA GPUs?

  While NVIDIA GPUs dominate AI workloads, we’re vendor-agnostic consultants. We evaluate NVIDIA (H100, A100, L40S, RTX series), AMD (MI300X, MI250), and Intel (Data Center GPU Max) options based on your specific use case, software compatibility, and budget. Our goal is finding the best performance per dollar for your requirements.

• What about storage and networking for AI workloads?

  High-speed storage and networking are critical for AI performance. We design complete infrastructure solutions including NVMe arrays for training data, parallel file systems for checkpointing, and high-bandwidth networking for distributed training. Our professional services ensure these components are properly sized and integrated with your GPU infrastructure.

• How do you stay current with rapidly evolving AI hardware?

  Our team continuously benchmarks new GPU releases, maintains relationships with major hardware vendors, and monitors real-world performance across diverse AI workloads. We participate in early access programs and maintain a testing lab where we validate new architectures. This ensures our recommendations reflect the latest price-performance optimizations available.