INFRASTRUCTURE
AI Infrastructure
Hardware, compute, deployment patterns, and cost optimization. We track GPU availability, inference economics, serving stacks, and the infrastructure decisions that determine whether AI projects ship or stall. Built for the engineers building the stack and the executives funding it.
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NVIDIA Blackwell Pricing Reshapes Inference Economics
NVIDIA's Blackwell B200 pricing drops inference costs 40% — but only if you redesign your serving stack.
Inference cost is the single biggest line item for AI-native companies. A 40% shift changes build-vs-buy math overnight.
Open-Source Serving Stacks: vLLM vs TGI vs TensorRT-LLM in 2026
Your choice of serving engine determines 30-60% of your inference cost. Here's which one wins for your workload.
Serving infrastructure is the largest variable cost in any AI deployment. Picking the wrong engine means leaving money and latency on the table.
Cloud GPU Pricing Shifts in Q1 2026
AWS, GCP, and Azure all adjusted GPU instance pricing this quarter. The savings are real, but only if you know where to look.
GPU compute is typically 60-80% of AI infrastructure spend. A 15-20% price shift changes the economics of every deployment decision.
Model Benchmarks Are Lying to You
That model scoring 92% on MMLU? It might perform 15-20 points lower on your actual workload. Here's why, and what to do about it.
Model selection drives architecture decisions, cost projections, and product timelines. If your selection is based on misleading benchmarks, everything downstream is wrong.
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