Silicon to System: Engineering Enterprise AI Beyond the GPU

In this episode, we look underneath enterprise AI deployments to explore what it takes to build dependable, production-grade business infrastructure. Moving past raw accelerator benchmarks, we break down why true AI scalability requires engineering the complete workflow—from silicon all the way to business execution. Key Topics Covered in This Episode: The “Silicon to System” Framework: Why chip throughput alone cannot predict business performance, and why metrics should shift toward cost per accepted task. Inference Mechanics Unpacked: The architectural trade-offs between the compute-intensive prefill phase and the memory-bandwidth-bound decode phase. Data Readiness & Security Boundaries: Why vector databases aren’t enough, and how to enforce user identity and data permissions strictly outside the model prompt. Full-Workflow Observability: Why a healthy model endpoint can still produce a failed business task, and how end-to-end tracing identifies true latency bottlenecks across retrieval, tools, and retries. Deployment Economics: Comparing managed model APIs, dedicated capacity, and self-hosting based on workload concurrency, latency budgets, and operating risk.