GPU Compute (AMD & NVIDIA)
Dedicated GPU compute for AI, ML training and inference, rendering and HPC — fitting operating models whose workloads are GPU-bound and whose economics cannot absorb hyperscaler GPU pricing, and whose architecture benefits from hands-on sizing of GPU class, interconnect and storage path rather than a self-service catalog.
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CPU Compute & Bare Metal
AMD and NVIDIA-class compute delivered as virtualized or dedicated bare metal — fitting operating models whose workloads are large enough that consumption-based public-cloud spend becomes hard to predict, and whose architecture benefits from predictable infrastructure cost and dedicated capacity.
Petabyte-Scale Block & Object Storage
High-performance block and object storage at petabyte scale — fitting operating models whose data footprint (training datasets, media, scientific output, web3 archives) outpaces self-service tier economics, and whose storage posture benefits from engineered storage paths rather than commodity tiers.
Web3 & Decentralized Storage
Web3-aligned data storage and infrastructure for decentralized applications and storage networks — fitting operating models whose product is built on distributed-storage primitives, and whose architecture benefits from a US-based operator with native web3 storage expertise.
Networking & PoP Footprint
Networking across San Diego, Las Vegas and Houston PoPs with direct-connect options — fitting operating models with a US-centric workload posture, latency requirements that benefit from West-Coast and South-Central footprint, and security postures that prefer dedicated network paths over generic internet egress.