A very large sparse model, hundreds of experts, and users on every continent. The layout that keeps experts close to compute, routes around regional skew, and decides what 'global KV cache' can and cannot mean.
Design GPT-scale MoE inference as a global service across regions. How do you lay it out?
A very large sparse model, hundreds of experts, and users on every continent. The layout that keeps experts close to compute, routes around regional skew, and decides what 'global KV cache' can and cannot mean.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
The seniority signal is refusing to spread one model's all-to-all across an ocean: experts stay within a low-latency fabric island, and the global layer is request routing plus failover, not cross-region tensor traffic. Strong candidates separate the per-token critical path (must be local) from the control plane (can be global) and are honest that a literally global KV cache is mostly a session-affinity and prefix-cache problem, not a distributed shared memory. Watch for the candidate who proposes synchronous cross-region all-to-all and never notices they've added 150ms per layer.
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