Insights & Resources
Perspectives From the Engineering Floor
Written by our specialists on the disciplines that decide whether AI succeeds in production — utilization economics, delivery gaps, private AI, performance measurement and hybrid design.
- AI Infrastructure
- Enterprise AI
- Private AI
- Inference Performance
- Hybrid Cloud
GPU Utilization Is an Economics Problem, Not Just an Engineering One
Low GPU utilization is usually treated as a tuning issue. In practice it is an economics issue — and the fixes live in scheduling, workload design and measurement discipline as much as in hardware.
Read perspectiveFrom Pilot to Production: The Gaps That Stall Enterprise AI
Most enterprise AI programs do not fail on model quality. They fail in the space between a successful proof of concept and a dependable production system. The gaps are predictable — and closable.
Read perspectivePrivate AI in Practice: What It Really Takes to Keep Inference Inside Your Boundary
Private AI is more than an on-premises server with a model on it. Keeping AI inside your trust boundary involves hardware, serving engineering, data controls and governance — each of which can quietly reintroduce outside dependencies if designed casually.
Read perspectiveInference Performance: The Metrics That Actually Matter
Tokens per second is the headline number, but production inference quality is decided by a family of metrics — and by whether they are measured under realistic concurrency. A practical guide to what to measure and why.
Read perspectiveHybrid AI: Designing for Data Gravity
Hybrid AI architectures succeed when they are designed around where data lives, not around where compute is fashionable. A framework for workload placement that respects data gravity, latency and cost.
Read perspectiveTurn Perspective Into Engineering.
These articles describe how we think. If the problems they describe are the ones you're facing, a conversation with our specialists is the practical next step.
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