Scope before you SKU
GPU infrastructure fails audits when teams buy cards without fabric, power, or storage context. Define workload class first — LLM pre-training, fine-tuning, batch inference, or mixed HPC — then map to node count, GPU model, host memory, and network tier.
Key design dimensions
- GPU and host: NVIDIA data-center GPUs on Supermicro or tier-one platforms with validated power and PCIe/NVLink topology.
- Network: East-west bandwidth for distributed training (InfiniBand or high-speed Ethernet) and north-south for storage and management.
- Storage: Parallel filesystem or NVMe tiers matched to checkpoint and dataset size.
- Facility: Rack power (kW), cooling, and phased delivery for data center or lab expansion.
Get a formal cluster quote
PuBuild engineers multi-node BOMs with lead times and compliance notes for commercial and government programs. Submit an RFQ or explore AI & machine learning solutions.