AI Infrastructure Requirements: Compute, Storage, and Networking

Deploying AI in the enterprise requires rethinking infrastructure. Unlike traditional software that runs efficiently on general-purpose servers, AI workloads — especially training and inference for large models — have specific compute, memory, storage, and networking requirements that differ significantly from typical enterprise workloads.

The Three Infrastructure Categories

Training infrastructure: Used to train or fine-tune AI models. Extremely compute-intensive. Most enterprises don't train foundation models from scratch — they use pre-trained models via API and may fine-tune for specific domains.

Inference infrastructure: Used to run trained models to generate predictions or responses. This is where the ongoing operational cost lives.

Data infrastructure: Pipelines, storage, and processing required to feed AI systems with clean, fresh training and operational data.

Compute Requirements

GPU vs. CPU

AI model inference with large language models requires GPU acceleration. CPUs alone are too slow for LLM inference at any reasonable latency or throughput.

Key GPU options for enterprise AI:

NVIDIA H100: Highest performance for both training and inference. Required for serious fine-tuning. $25,000–$35,000 per unit.
NVIDIA A100: Previous generation; still excellent for inference. More available and lower cost.
NVIDIA L40S: Optimized for inference; good price/performance for serving applications.
AMD Instinct MI300X: Competitive with H100 for LLM inference; growing ecosystem support.

Most enterprise deployments don't need GPUs on-premises — they access frontier models (GPT-4, Claude, Gemini) via API, and the GPU infrastructure is the provider's concern.

On-premises GPU infrastructure makes sense for:

Organizations with strict data residency requirements
High-volume, latency-sensitive inference where API costs exceed on-premises costs
Organizations fine-tuning proprietary models on sensitive data

Cloud Compute Options

All three major cloud providers offer managed AI compute:

| Provider | GPU Instances | Managed Inference | LLM APIs | |---|---|---|---| | AWS | P4d, P5 (H100) | SageMaker | Bedrock | | Azure | NDv4, NDmv4 | Azure AI | Azure OpenAI | | GCP | A3 (H100) | Vertex AI | Gemini API |

For most enterprises, the right answer is managed API access (Bedrock, Azure OpenAI, Vertex AI) rather than managing raw GPU instances. This removes infrastructure management overhead while delivering state-of-the-art capability.

Sizing for Inference Workloads

If self-hosting models (open-source or fine-tuned), size based on:

Model parameter count: Larger models need more GPU memory
- 7B parameters: 1–2 GPUs (A10G)
- 70B parameters: 4–8 H100s
- 175B+ parameters: 8–16 H100s
Concurrent request throughput: Each request requires processing capacity
Latency requirements: Lower latency needs more GPUs running fewer tokens/request

Memory Requirements

GPU VRAM is the binding constraint for LLM deployment.

| Model Size | VRAM Required | Example Hardware | |---|---|---| | 7B (4-bit quantized) | 8 GB | 1× RTX 4090 | | 13B (4-bit) | 16 GB | 1× A10G | | 70B (4-bit) | 48–80 GB | 2–4× A10G | | 70B (full precision) | 140 GB | 2× H100 80GB |

System RAM requirements are less constraining but still matter for data processing pipelines — 64–256 GB RAM is typical for AI application servers.

Storage Requirements

AI workloads have three distinct storage tiers:

Hot Storage (NVMe SSD)

Model weights and embeddings actively served in inference
Feature stores for real-time feature retrieval
Low latency (under 1ms) required
Typical scale: 1–10 TB per major model deployment

Warm Storage (Object Storage / NAS)

Training datasets, evaluation datasets
Model checkpoints and versions
RAG knowledge base documents (before vectorization)
Typical scale: 10 TB – 1 PB depending on data volume

Cold Storage (Archive / Glacier)

Historical training data
Model version archives
Audit logs (must be immutable; 5–7 year retention common in regulated industries)
Cost-optimized; access time measured in minutes

Vector Database Storage

For RAG applications, vector databases store embedding representations of your knowledge base. Scale estimates:

1M documents with 1536-dimension embeddings ≈ 6 GB
100M documents ≈ 600 GB

Managed vector databases (Pinecone, Weaviate Cloud) handle infrastructure. Self-hosted (Qdrant, Chroma) requires dedicated server resources.

Networking Requirements

High-Throughput Internal Networking

For multi-GPU inference clusters, GPU interconnect bandwidth is critical:

NVIDIA NVLink: 600 GB/s per GPU pair (within-node)
InfiniBand (200–400 Gb/s): Required for multi-node model serving

For cloud deployments, instance types with high interconnect bandwidth (e.g., p4d.24xlarge with 400 Gbps networking) are required for multi-node inference.

API Traffic Planning

Enterprise AI applications make many API calls — to LLM providers, vector databases, and internal systems. Network requirements:

Each GPT-4 API call: ~5–50 KB payload, 200–2000ms response
High-volume production deployments: Plan for 100–10,000 API calls/minute
Required: reliable egress with >99.9% uptime; rate limit alerts

Data Pipeline Networking

Training data pipelines require sustained high-throughput transfer from primary data stores to processing infrastructure. For large datasets:

10 Gbps dedicated pipeline links are common
Consider data proximity (compute close to data reduces transfer costs and latency)

On-Premises vs. Cloud vs. Hybrid

When to Choose Cloud (Most Enterprises)

API-based access to foundation models (GPT-4, Claude, Gemini)
Variable workloads with unpredictable peak demands
No strict data residency requirements
Fastest time to value; no hardware management

When to Consider On-Premises

Strict data sovereignty or residency requirements (financial services, defense, healthcare in some jurisdictions)
Very high and stable inference volumes where on-premises unit economics exceed cloud
Regulatory requirements for full infrastructure control

Hybrid Architecture

On-premises for sensitive data processing and inference
Cloud for non-sensitive workloads and burst capacity
Consistent orchestration layer across both

Cost Estimation Framework

API-based approach (most common):

GPT-4o: ~$5/1M input tokens, $15/1M output tokens
Claude 3.5 Sonnet: ~$3/1M input, $15/1M output
At 100,000 API calls/day with 2,000 tokens avg: ~$3,000–6,000/month in model API costs alone

Self-hosted inference:

4× NVIDIA H100 server: $400,000–600,000 capex
Power + cooling + colocation: $5,000–8,000/month
Breakeven vs. API typically requires very high sustained volume

For most enterprises: start entirely cloud/API, monitor costs at scale, consider partial on-premises only when API costs exceed $50K/month and workloads are stable.