capacity

Build a cluster power budget with clear equipment boundaries, facility overhead and failover requirements. Distinguish energy use from reserved electrical capacity.

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.

Estimate replacement demand over replenishment time, then size for shortage risk and failure correlation. Distinguish shelf parts from runnable spare nodes.

Choose a supported tenant boundary, secure the surrounding services, and verify cleanup before reassignment. Calculate fragmentation from actual requests.

Plan a GPU-generation migration around usable workload capacity, site readiness, compatibility and a tested rollback path.

Survey real facility headroom, phase work around shared dependencies, and compare retrofit with relocation using useful capacity and total transition cost.

Compare the old fleet's useful value with operating costs and resale, verify its health, and complete data handling before reassignment or transfer.

Separate the 100B memory floor from the training deadline, then compare two 1,024-GPU layouts with their actual state ownership and communication schedules.

Follow a token into retained attention state, derive bytes per position for a 70B model, and distinguish present cache usage from the room needed to finish a response.

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.

Separate checkpoint memory, expert assignments and retained MLA state. The active parameter count alone cannot predict per-rank fit or token latency.

Translate request demand into prompt work, generated tokens and retained state, then size a measured serving configuration and its failure reserve.

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.

Derive a prompt-plus-output contract, calculate per-rank state capacity, and measure the concurrency that meets latency and overload targets.

Long prefills coincide with decode stalls. Compare a tuned combined pool with separate phases, derive transfer and pool capacity, and test whether the improvement survives connector failures.

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.

Confirm that requests are waiting for usable state capacity. Separate allocated memory, live blocks and evictable prefixes, then compare admission, lower-precision state, prefix sharing and hardware changes with explicit assumptions.

Price the same workload and service guarantees, including input tokens, idle capacity, failures and operations. Then check whether the algebraic crossover fits the fleet before treating it as a buying decision.

Design a shared catalogue with explicit service tiers, reproducible deployments and bounded cold starts. Residency depends on burst behavior and resource reuse; placement depends on GPU groups and interference as well as memory.

Budget growing attention state, bounded recurrent state and retained prefix snapshots separately. Work the short-context crossover, then test whether the cache manager resumes every layer from the same prefix boundary.

Define cold and reused-context objectives, verify the checkpoint's real state layout, and size per-rank memory and transfer demand. Caching and phase separation help only when their compatibility, retention and capacity assumptions hold.

Follow media through fetching, preprocessing, encoding and language-model execution. Derive visual positions from the actual processor output, account for encoder placement and separate the caches and latency stages that text-only monitoring misses.

Screen exact checkpoints for hard requirements, then compare deployable candidates on held-out product tasks, service limits and total cost. Smaller artifacts, leaderboard scores and token rates each answer only part of the decision.

Turn a benchmark number into a bill that survives real utilization, then write the serving contract the gateway enforces and the evaluation the tuned configuration still owes.

Can one storage system serve datasets and checkpoints? Follow a save from staging to recovery, calculate its budget beside protected reads, and learn when separate paths actually solve the contention.

Size usable inference headroom from the complete readiness delay and demand ramp, then decide which training capacity can safely be borrowed.

Measure throughput at several power limits, translate the facility target into a GPU budget, and account for slower ranks before changing the fleet.

Follow cache hits and misses into shard calls, rerank pairs and generation work; size consistent branches and enforce access-aware reuse.

Derive adapter bytes from the target matrices, share compatible base weights, and keep each request's cache identity separate. A worked pool design avoids applying the mixed-adapter cost twice.

Divide one token budget across gateways instead of copying it. Follow output reservation and settlement, then handle grants, retries and outages without inventing extra burst capacity.

Remove the largest region from a concrete capacity plan. Check the surviving budget, tenant eligibility, cold prefixes and retry semantics before claiming the service can fail over.

Clarify what the 256-GPU request must accomplish, then compare it with real placement and quota constraints. Offer a feasible alternative and explain who can decide when teams’ priorities conflict.

Separate a disagreement about measurements from a real capacity shortage. Work a reclaim example, expose the missing reserve and escalate an authorized decision.

Evaluate a proposed 2,000-GPU purchase using compatible capacity, dated demand and complete alternatives. Make any gap left by reclaim visible.

Report a capacity shortfall with assumptions, consequences, feasible options and a decision deadline. Show how one uncertain project changes the forecast.