GPU Infrastructure for AI
Serving models at scale means managing GPU allocation, batching requests, and deciding between shared and dedicated capacity.
Prerequisites
Overview
Knowing what a GPU is is different from operating GPU capacity for inference at scale — batching requests to keep utilization high, deciding between shared and dedicated instances, and managing the cost of what is usually the most expensive line item in an AI system.
Where It Fits
Incoming Requests
Request Batching
GPU Capacity
Shared or dedicatedResponses
Key Points
- Request batching
- Grouping multiple inference requests together improves GPU utilization compared to processing one at a time.
- Shared vs. dedicated
- Shared GPU capacity (through a hosted API) trades cost efficiency for less control than dedicated, self-managed GPUs.
- Cost as the constraint
- GPU capacity is typically the largest cost driver in a self-hosted AI system, more than the application layer around it.
Interview Question
Why does request batching matter for GPU inference specifically?
A GPU processes a batch of inputs nearly as fast as a single input, up to a point — so serving requests one at a time wastes most of the GPU’s throughput. Batching multiple requests together significantly improves utilization and lowers cost per request, which is why inference servers are usually designed around it rather than naive one-request-at-a-time handling.
Explain It in 30 Seconds
Serving models at scale means managing GPU capacity deliberately — batching requests for utilization, choosing between shared and dedicated capacity, and treating GPU cost as the primary constraint of a self-hosted AI system.
Real-World Stack
Technologies commonly used to implement this in production.