Mercan Technology Insights
Why capacity planning matters
A GPU choice does not by itself define an AI service. Model size, concurrency, latency needs, data location and the surrounding API layer affect the architecture.
What should teams evaluate?
- Separate training, tuning and inference workloads.
- Measure expected concurrency and observe actual model-serving demand.
- Define access, logging and data-handling boundaries before exposing an API.
Mercan perspective
Assess the workload before committing to dedicated capacity. GPU infrastructure, model serving and managed API controls can be scoped as separate service layers.