Infrastructure Questions

Q: What GPU hardware do I need for enterprise AI?

It depends on your workload. For large-scale model training and fine-tuning of 70B+ parameter models, NVIDIA H100 SXM5 (80GB) or H200 (141GB) are the current production standards. For inference serving, NVIDIA L40S (48GB) offers better cost-per-token economics. For smaller models and lighter workloads, A100 80GB remains widely deployed. The right answer depends on your specific model sizes, throughput requirements, and budget.

Q: How much GPU memory do I need?

GPU memory capacity determines the maximum model size you can run. A rough guide: a 7B parameter model in FP16 requires ~14GB; a 13B model requires ~26GB; a 70B model requires ~140GB (requiring multi-GPU serving or quantization). For training, you need additional memory for optimizer states, gradients, and activations — typically 3–4x the model size in FP32 training.

Q: Do I need InfiniBand or can I use Ethernet for AI?

For multi-node training clusters, InfiniBand NDR (400 Gb/s) provides the best performance for all-reduce operations. However, RoCEv2 over 400GbE has closed the gap significantly and is a viable alternative for many enterprise training workloads at lower cost. For inference-only deployments, standard 100GbE or 25GbE is typically sufficient.

Q: What storage throughput do I need for AI?

A general guideline: plan for 5–10 GB/s of aggregate storage throughput per 8-GPU server for training workloads. A 32-GPU cluster needs 20–40 GB/s minimum. NFS is generally insufficient for GPU-dense clusters — parallel file systems (GPFS, Lustre, WEKA) are required for large-scale training.

Q: How much power does AI infrastructure consume?

Modern AI servers draw 8–10 kW per server (8x H100 configuration). NVIDIA GB200 NVL72 rack systems draw up to 120 kW per rack. A 10-rack AI cluster requires 800 kW–1.2 MW of facility power capacity. This is 5–10x the power density of traditional IT infrastructure and requires significant facility planning.

Q: Do I need liquid cooling for AI infrastructure?

For current-generation H100/H200 servers at 8–10 kW per server, rear-door heat exchangers or high-capacity CRAC/CRAH can work up to 20–25 kW per rack. For next-generation GB200 NVL72 systems at 120 kW per rack, direct liquid cooling is required — air cooling is not supported. If you are planning infrastructure for 3–5 years, design for liquid cooling capability.

Deployment Questions

Q: What is the difference between private AI and on-premises AI?

Private AI is a deployment philosophy — AI workloads run within the organizational security boundary, with full control over data and models. On-premises AI is a location — infrastructure physically located in your data center or a colocation facility. Private AI can be implemented on-premises, in a dedicated private cloud, or in a colocation facility. The key characteristic is that data and models do not leave the organizational boundary.

Q: When should I use cloud AI vs. on-premises AI?

Use cloud AI for: early-stage exploration, intermittent workloads (less than 4 hours/day), use cases without data sovereignty requirements, and when time-to-market is critical. Use on-premises AI for: sustained high-volume workloads, regulated industries with data residency requirements, proprietary model training, and when 3-year TCO analysis favors on-premises (typically at 12+ hours/day utilization).

Q: How long does it take to deploy enterprise AI infrastructure?

A typical enterprise AI infrastructure deployment takes 3–6 months from procurement to production: 1–2 months for procurement and delivery (longer if GPU lead times are extended), 1–2 months for installation, integration, and testing, and 1–2 months for software stack deployment and validation. Total time from strategy to first production AI workload is typically 6–18 months including use case development and governance setup.

Q: Can I run AI workloads in a colocation data center?

Yes, colocation is a common deployment model for enterprise AI. Key requirements: sufficient power density (10+ kW per cabinet, ideally 20–30 kW for AI-dense deployments), liquid cooling availability or high-capacity air cooling, low-latency connectivity to your corporate network, and physical security controls. Not all colocation facilities can support AI-density power requirements — verify specifications before committing.

Model & Software Questions

Q: Should I train my own AI model or use a foundation model?

For almost all enterprise use cases, fine-tuning a foundation model (Llama, Mistral, Falcon) on your proprietary data is more cost-effective than training from scratch. Training a competitive large language model from scratch requires thousands of GPUs and hundreds of millions of dollars. Fine-tuning a 7B–70B parameter model on domain-specific data requires 8–64 GPUs and weeks of compute time. Train from scratch only if you have a unique data advantage and the resources to compete with frontier model labs.

Q: What is RAG and when should I use it?

Retrieval-Augmented Generation (RAG) combines a language model with a retrieval system that fetches relevant documents from a knowledge base before generating a response. Use RAG when: your use case requires up-to-date information beyond the model's training cutoff, you need to ground responses in specific organizational documents, or you want to reduce hallucination in domain-specific applications. RAG is often more cost-effective than fine-tuning for knowledge-intensive applications.

Q: What MLOps platform should I use?

The right MLOps platform depends on your scale and existing infrastructure. For experiment tracking and model registry: MLflow (open source, widely adopted) or Weights & Biases (commercial, excellent UX). For pipeline orchestration: Kubeflow (Kubernetes-native) or Apache Airflow. For model serving: NVIDIA Triton Inference Server (production-grade, multi-framework) or vLLM (optimized for LLM serving). Most enterprises use a combination of these tools rather than a single platform.

Governance & Compliance Questions

Q: What regulations apply to enterprise AI?

Applicable regulations depend on your industry and geography. Key frameworks: EU AI Act (risk-based classification, binding requirements for high-risk AI in the EU), US financial services SR 11-7 (model risk management for banks), FDA AI/ML guidance (software as a medical device), GDPR Article 22 (automated decision-making rights), and emerging US state AI laws. Most enterprises in regulated industries should assume AI governance requirements will increase and build governance infrastructure accordingly.

Q: What is model drift and how do I manage it?

Model drift occurs when real-world data distributions shift away from training data, causing model performance to degrade over time. Two types: data drift (input feature distributions change) and concept drift (the relationship between inputs and outputs changes). Manage drift through: continuous monitoring of model performance metrics, statistical drift detection on input features, defined retraining triggers, and scheduled periodic model reviews.

Q: How do I handle AI explainability requirements?

Explainability requirements vary by use case and regulation. For credit decisions (ECOA, FCRA), you must be able to provide specific reasons for adverse actions. For EU AI Act high-risk systems, you must provide meaningful explanations of automated decisions. Technical approaches: SHAP values for feature importance, LIME for local explanations, and attention visualization for transformer models. For the highest-stakes decisions, consider inherently interpretable models (decision trees, logistic regression) that sacrifice some performance for full transparency.

Cost & ROI Questions

Q: How much does enterprise AI infrastructure cost?

A starter enterprise AI cluster (8x H100 80GB server with networking and storage) costs $400K–$600K in hardware. A production-scale cluster (32–64 GPUs with full networking, storage, and software stack) costs $2M–$8M. Add 20–30% for software, professional services, and facility upgrades. Cloud equivalent costs: $25–$35/hour on-demand for an 8x H100 instance, or $15–$20/hour reserved. At sustained utilization, on-premises typically achieves lower TCO after 12–18 months.

Q: What GPU utilization rate should I target?

Target 70–80% utilization for training clusters and 60–70% for inference clusters (leaving headroom for traffic spikes). Utilization below 50% indicates significant waste — investigate workload scheduling, cluster sharing policies, and whether the cluster is appropriately sized for actual demand. Utilization consistently above 85% indicates capacity constraints that may be limiting productivity.

Q: How do I measure AI ROI?

Connect AI program costs to specific business outcomes: cost reduction (measure actual cost savings vs. pre-AI baseline), revenue generation (measure incremental revenue attributable to AI recommendations or automation), risk reduction (quantify fraud losses prevented, compliance violations avoided), and productivity improvement (time savings × fully loaded labor cost). Report ROI at both the program level and individual use case level. Programs without clear ROI measurement consistently face budget cuts.

Getting Started Questions

Q: Where should I start with enterprise AI?

Start with use case identification, not infrastructure. Identify 3–5 high-value business problems where AI can deliver measurable outcomes. Assess data readiness for those use cases. Then determine infrastructure requirements based on workload profiles. Organizations that start with infrastructure procurement before use case clarity consistently underutilize their investments.

Q: How do I assess my organization's AI readiness?

Evaluate eight dimensions: data quality and availability, compute infrastructure, ML talent and capability, governance and risk management maturity, executive sponsorship, use case pipeline, change management capability, and budget and investment commitment. DCS Global's AI Readiness Assessment provides a structured evaluation across all eight dimensions with specific recommendations for each gap.

Q: What is the first AI use case I should deploy?

The best first use case has three characteristics: high business value (clear, quantifiable ROI), technical feasibility (good data quality, mature model approaches), and organizational readiness (a business unit champion who will drive adoption). Common high-success first use cases: document intelligence (contract review, invoice processing), predictive maintenance, customer service automation, and demand forecasting. Avoid starting with use cases that require regulatory approval or affect high-stakes decisions — build organizational AI capability on lower-risk applications first.