
Hands-on architecture, sizing, deployment and migration engineering — fitting operating models whose infrastructure footprint is meaningful enough that talking to engineers about workload shape is more useful than a console, and whose migration posture (on-prem to cloud, cloud to cloud, hybrid) benefits from engineered cutover rather than lift-and-shift.
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More from PiKNiK
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.
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.
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-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.