Cloud, Datacenters & AI Infrastructure

Why AI needs serious infrastructure

Training GPT-scale models costs millions in compute. Even fine-tuning a medium model or serving thousands of daily chat users requires reliable servers, GPUs, networking, and monitoring.

Datacenters are buildings full of racks, power, cooling, and fiber connections — the physical home of "the cloud."

Training vs inference

• check_circleTraining — building the model from data. Compute-heavy, batch-oriented, uses GPU clusters for weeks.
• check_circleInference — running the trained model on new inputs. Must be fast and cost-efficient (milliseconds per request).
• check_circleEdge inference — running smaller models on phones or local devices (privacy, offline use).

GPUs, TPUs, and specialized chips

NVIDIA GPUs (A100, H100) dominate AI training. Google TPUs are alternative accelerators. Apple Neural Engine runs on-device inference.

VRAM (GPU memory) limits model size you can load. Larger models need multi-GPU setups with parallelization techniques.

Cloud platforms for AI

Most startups use managed APIs (OpenAI, Anthropic) before investing in self-hosted infrastructure.

• check_circleAWS — SageMaker, EC2 GPU instances, Bedrock (managed LLMs)
• check_circleGoogle Cloud — Vertex AI, TPU pods, Gemini APIs
• check_circleMicrosoft Azure — OpenAI partnership, Azure ML
• check_circleSpecialized hosts — Lambda Labs, CoreWeave for GPU rentals

MLOps — DevOps for machine learning

Without MLOps, models work in notebooks but break in production. DrayenGroup implements these practices when shipping AI to clients.

• check_circleExperiment tracking — MLflow, Weights & Biases log runs and metrics
• check_circleModel registry — version and approve models for production
• check_circleCI/CD pipelines — automated testing and deployment
• check_circleMonitoring — latency, error rates, data drift, model degradation
• check_circleFeature stores — consistent data transformations online and offline

Datacenter anatomy

• check_circleRacks — standardized cabinets of servers
• check_circlePower & cooling — AI racks draw 10–100 kW each; liquid cooling emerging
• check_circleNetworking — high-speed fabrics (InfiniBand) move data between GPUs
• check_circleRegions & availability zones — geographic redundancy (eu-west, af-south-1 Cape Town for Africa latency)
• check_circleSustainability — energy use and carbon footprint under scrutiny globally

From laptop to production — a realistic path

1. Prototype with API (ChatGPT) on your laptop. 2. Build app backend integrating API. 3. Add RAG with your documents. 4. Monitor usage and costs. 5. Fine-tune or self-host only when scale or privacy demands it.

Businesses in Kigali can start today with APIs and mobile-friendly apps — datacenter-scale training is for model builders, not most SMEs.