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Frequently Asked Questions
What infrastructure does enterprise AI require?
Enterprise AI requires GPU servers (NVIDIA H100/H200 or equivalent), high-bandwidth networking (InfiniBand or 400G Ethernet), parallel file storage (GPFS, Lustre, or NVMe-oF), redundant power (N+1 or 2N UPS), and precision cooling (liquid or immersion for high-density racks). A minimum viable AI cluster for production workloads typically starts at 8 GPUs with 3.2 Tbps interconnect bandwidth.
How much does enterprise AI infrastructure cost?
A production-grade 8-GPU cluster (NVIDIA H100 SXM5) costs $2.5M–$4M in hardware alone. Add 30–40% for networking, storage, power, and cooling infrastructure. Total 3-year TCO including operations typically runs $5M–$8M for an 8-GPU cluster. Cloud alternatives cost $25–$35/GPU-hour, making on-premises more cost-effective at sustained utilization above 40%.
What is the difference between enterprise AI and consumer AI?
Enterprise AI is distinguished by scale (thousands of GPUs vs. single devices), governance requirements (audit trails, access controls, data lineage), reliability standards (99.99% uptime SLAs), security architecture (air-gapped options, data sovereignty), and integration complexity (ERP, CRM, and legacy system connectivity). Consumer AI tools like ChatGPT are built on shared infrastructure with no enterprise controls.
How long does it take to deploy enterprise AI infrastructure?
A greenfield AI data center takes 18–36 months from design to production. Retrofitting existing data center space for AI workloads takes 6–12 months. Deploying a pre-configured AI cluster in colocation takes 3–6 months. Cloud-based enterprise AI can be operational in days but requires 3–6 months for proper governance and integration.
What AI governance frameworks should enterprises use?
Leading enterprises adopt NIST AI RMF (Risk Management Framework), ISO/IEC 42001 (AI Management Systems), EU AI Act compliance frameworks (for European operations), and MITRE ATLAS (adversarial threat modeling). Most organizations layer these with internal policies covering model cards, data governance, bias testing, and incident response.
Key Terms
A group of GPU servers interconnected with high-bandwidth networking (InfiniBand or RoCE) to function as a single computational unit for AI training and inference workloads.
A large AI model trained on broad data that can be fine-tuned for specific enterprise tasks. Examples include GPT-4, Claude, Llama, and Mistral.
The process of running a trained AI model to generate predictions or outputs. Inference workloads are latency-sensitive and require different infrastructure than training.
Adapting a pre-trained foundation model on domain-specific data to improve performance on enterprise-specific tasks without training from scratch.
An architecture that combines a vector database with a language model, allowing the AI to retrieve relevant enterprise documents before generating responses.
A standardized document describing an AI model's intended use, performance metrics, limitations, and ethical considerations — required for enterprise AI governance.
Buyer\'s Guide
Questions to ask when evaluating enterprise ai solutions.
Why it matters: Training LLMs requires NVLink-connected H100/H200 SXM5 GPUs. Inference can use PCIe variants or AMD MI300X. Mismatching GPU architecture to workload wastes 40–60% of compute budget.
Red flag: Vendors who recommend the same GPU for all workloads without workload analysis.
Why it matters: Modern AI servers draw 10–30 kW per rack. Standard data centers support 5–8 kW/rack. Deploying AI in under-provisioned facilities causes thermal throttling and hardware failures.
Red flag: Facilities that cannot demonstrate per-rack power metering and cooling capacity calculations.
Why it matters: InfiniBand HDR/NDR (200–400 Gbps) reduces all-reduce communication overhead by 60–80% vs. standard Ethernet. For clusters above 8 GPUs, fabric choice determines training throughput.
Red flag: Proposals that use 25GbE or 100GbE for GPU interconnect without justification.
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