AI Infrastructure Insights

The infrastructure layer is where AI products succeed or fail at scale. Model quality gets the headlines, but infrastructure determines latency, cost, reliability, and whether your system survives Black Friday traffic. These insights cover the decisions that matter most.

What This Track Covers

Cloud Architecture — When to use managed services versus self-hosted infrastructure. How to design for failover and multi-region deployment. Cost modelling at different scales.

GPU and Compute — Choosing the right hardware for inference versus training. Understanding memory bandwidth bottlenecks. When unified memory (Apple Silicon) makes sense versus discrete GPUs.

Model Serving — vLLM, TensorRT-LLM, SGLang, and managed inference platforms. Continuous batching, tensor parallelism, and quantisation for production workloads.

Vector Databases — pgvector, Pinecone, Weaviate, Qdrant — performance characteristics, scaling limits, and when each makes sense. Embedding model selection and indexing strategies.

Scaling Patterns — Auto-scaling inference, caching layers, load balancing, and the architecture patterns that let you go from 100 to 100,000 users without rebuilding.

Featured Insights