AI inference economics: why the GPU-hour is only the starting point
Reasoning and long-context workloads make latency, memory and useful output central to compute procurement.
· Arvica Cloud · Analysis & buying guide
What is changing
NVIDIA Dynamo documents a serving design that separates prompt processing (prefill) from token generation (decode), with a KV-cache transfer between them. This is a concrete example of infrastructure being organised around stages of an inference request, rather than one undifferentiated GPU pool. [1]
Arvica analysis: buy a service outcome
Our view is that a buyer should define the service target before comparing hardware prices. For an interactive assistant, record time to first token, output-token speed, peak concurrency and an acceptable error rate. For overnight document processing, completion time and total batch cost may matter more. An inexpensive instance that misses the service target is not a valid like-for-like alternative.
A benchmark brief that can be compared
Use the same model revision, precision, input/output length distribution, concurrency and software version in each trial. Record successful requests and rejected or timed-out requests separately. Include warm-up, cache behaviour and idle time in the report. Divide total trial cost by successful work, not by a theoretical peak token rate. No throughput or cost saving is assumed here: these are measurement conditions to agree with the operator.
When complexity is worth paying for
Before requesting separate serving pools, establish a simple baseline. Ask what extra network, orchestration and operational effort the proposed design requires. A small, irregular workload may favour a simpler configuration. For a larger stable workload, test whether isolating stages improves the agreed service targets enough to justify those costs. Send Arvica the workload profile and measurement targets so the quote can name a suitable configuration and acceptance test.
Build a representative inference test with model, precision, context, concurrency and quality targets. Connect the measured result to a transparent GPU budget.
Download an editable worksheet to agree scope, compare configurations and decide whether to stop, adjust or scale. An evaluation method, not a customer case study.
