AI Inference Systems

Inference vs. Training Infrastructure

While training infrastructure prioritises raw compute throughput, inference infrastructure prioritises latency, availability, and cost efficiency. Inference GPUs can be lower-tier (e.g., inference-class GPU rather than prior-generation GPU), deployed in smaller clusters (1–8 GPUs per node), and connected via standard networking rather than InfiniBand.

However, aggregate inference demand typically exceeds training demand once models reach production scale. A model trained on 10,000 GPUs may serve millions of daily queries across thousands of inference GPUs — making inference the larger long-term infrastructure market.

Serving Architecture

Modern inference systems use specialised serving software — GPU inference serving software, vLLM, TensorRT-LLM, or TGI — that optimises GPU memory usage, batches requests for throughput, and manages model versioning. These serving frameworks enable a single GPU to handle 100–1,000+ concurrent inference requests, dramatically reducing per-query costs.

Auto-scaling is critical: inference demand is bursty (traffic spikes during business hours, events, or viral moments). Inference systems must auto-scale GPU capacity within 30–60 seconds to handle traffic spikes without latency degradation, then scale down during low-traffic periods to control costs.

Geographic Distribution

Production inference requires geographic distribution to serve users with acceptable latency. A user in Mumbai querying an AI model hosted in Virginia experiences 150–250ms round-trip latency — acceptable for chat but unacceptable for real-time applications. Constellation's MENA-India deployment provides inference capacity in the UAE and India, serving 2+ billion users in the region with sub-50ms latency.

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