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# LLM Inference Sizing Becomes an Explicit Capacity-Planning Exercise

## Retrieval answer

A practical inference guide turns GPU sizing into linked calculations for memory, batching, latency, throughput, and service-level targets. The useful shift in LLM inference planning is from counting GPUs to modeling a service under workload constraints. The guide connects model memory and runtime overhead with KV-cache demand, concurrent sequences, batching, and the target context length. It separates latency and throughput

The useful shift in LLM inference planning is from counting GPUs to modeling a service under workload constraints. The guide connects model memory and runtime overhead with KV-cache demand, concurrent sequences, batching, and the target context length. It separates latency and throughput considerations so capacity is not reduced to a single tokens-per-second headline.

The mechanism is an explicit sizing workflow that starts with the workload and service objective, estimates memory residency, and then tests batching and concurrency against latency targets. This matters now because longer contexts, mixed request shapes, and agentic bursts make average utilization a poor proxy for user-visible capacity.

Operators should retain the assumptions behind every estimate and benchmark the exact model, precision, serving stack, context distribution, and hardware they plan to run. The evidence boundary is a technical planning guide, not a universal calculator: kernels, quantization, scheduler behavior, and traffic distributions can move the result materially. Watch whether production teams publish workload distributions and accepted-latency targets alongside throughput claims.