Why it matters
For teams deploying enterprise AI models into production, storage and memory bandwidth are replacing raw compute as the primary bottlenecks for latency and operational expense.
Key points
- Enterprise AI economics are centring on token generation costs rather than initial training runs.
- System architectures require a ground-up redesign of memory hierarchies to sustain low-latency inference.
- High-throughput storage tiers are becoming critical for serving commercial inference at enterprise scale.
