20Design the control plane for a GPU cloud: tenancy, provisioning, health, billing, and the API a customer sees.▼hardNewCoreWeaveNebiusLambda4 replies○ sign inA GPU cloud sells whole nodes and fabric slices to tenants who expect them in minutes and expect them to work. The node state machine, the reconciler that keeps thousands converging, the isolation lines on shared fabric, the health loop that pulls a bad node first, and billing that survives a control-plane outage.Open full answer →
27Design token-level billing and metering for an LLM API: count at the engine, aggregate, reconcile, and survive the failure modes.▼mediumNewOpenAITogether AI4 replies◆ premiumBilling is the one pipeline where a dropped event is money and a duplicated one is a support ticket. Where tokens are counted and by whom, the usage event and its idempotency key, the aggregation that closes a month, the reconciliation that proves the ledger against the engines, and the six ways it goes wrong.Open full answer →
01Given usage records of GPU allocations, compute what each tenant owes. Write it, and say what you would test.▼easy★ EssentialNewOpenAI4 repliesunlockedThree decisions decide whether this is correct: the numeric type, how partial hours are handled, and what happens to a record that does not make sense. The implementation with its tests, the floating-point trap that costs real money, and the questions to ask before writing any of it.Open full answer →
19Compute per-tenant GPU-hours for a billing period from a log of allocation start and stop events.▼mediumNewCoreWeaveModal4 replies○ sign inThe arithmetic is one multiplication and the difficulty is entirely in the events that do not pair cleanly. Clipping to the window, an allocation still running when the period ends, a stop with no start, and why every anomaly must be reported rather than dropped.Open full answer →