AI Infrastructure
AI infrastructure refers to the complete stack of hardware, software, and services required to develop, train, deploy, and scale artificial intelligence and machine learning workloads — from GPU clusters and high-speed networking to MLOps platforms and inference serving systems.
AI infrastructure has become the critical bottleneck for organizations looking to develop and deploy AI capabilities. The stack spans several layers: compute hardware (GPUs from NVIDIA, custom silicon from Google and Amazon), networking (high-bandwidth, low-latency interconnects like InfiniBand), storage (high-throughput parallel file systems), and software (orchestration, experiment tracking, model serving).
The build vs. buy decision is complex. Public cloud GPU instances offer flexibility but can be expensive at scale. Owned or leased GPU clusters reduce per-hour costs but require significant upfront capital and operational expertise.
Many organizations adopt a hybrid approach: cloud for experimentation and burst capacity, owned/collocated infrastructure for steady-state production workloads. Working with an advisor who understands both the technical requirements and the vendor landscape can help navigate procurement and negotiate favorable terms.
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Rebar brings 20+ years of operator experience to help you navigate these decisions. No pitch deck required.
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