sizing

Build a storage budget that includes retained checkpoints, the next save, overlapping datasets and failure protection. Then check whether the same tier can meet write and restore deadlines.

Budget the full megawatt, fit whole servers, and compare useful training throughput under measured power limits. A worked cap example shows when more GPUs help.

Work from each GPU's cache budget, reserve room for generated tokens, then check latency separately. A limiting-rank example shows why fitting today's prompts is not enough to admit tomorrow's work.

Derive a deployment candidate from the official tensor index, then account for latent-cache replication and the product workload. A worked example shows why enough aggregate HBM can still leave every GPU out of memory.

Turn public metadata into a deployment candidate with explicit uncertainty. Inspect architecture, stored formats and engine support, then work a per-rank memory example that explains why an aggregate fit can fail.

Use the actual tensor index to size Kimi K3, then separate a published hardware starting point from production capacity. Account for mixed precision, hybrid recurrent state, parser behavior, and the workload behind a throughput claim.

The output forecast is a starting point. Match the prompt and state workload, derive replica counts from an SLO-tested rate, check simultaneous loss scenarios and keep the cost denominator consistent with the forecast.