AI inference performance depends on more than model size or GPU speed. Network distance, request handling, serving strategies, cache behavior, and scaling all affect how quickly an application responds and how efficiently it runs.
As AI workloads move from experimentation to production, teams need to make infrastructure decisions that balance latency, throughput, GPU utilization, and cost. This eBook explains how the layers of the inference stack shape those outcomes.
It focuses on the architecture behind fast AI inference, from workload placement and model serving to caching, load balancing, and autoscaling.
What You Get in this eBook?
This eBook connects AI inference concepts with practical infrastructure decisions, helping you:
- Understand which performance metrics matter for different workloads, including time to first token, throughput, tail latency, and cost.
- See how region and network placement affect responsiveness and data residency.
- Learn how prefill, decode, continuous batching, KV cache, and streaming shape inference performance.
- Understand how caching and GPU memory management affect concurrency and throughput.
- Plan for request routing, load balancing, queuing, and autoscaling as demand changes.
- Evaluate the infrastructure considerations involved in moving AI inference into production.
Who Should Download This eBook?
This eBook is written for the teams responsible for building and operating AI applications, including:
- CTOs, CIOs, and engineering leaders
- AI/ML engineers and LLM application developers
- DevOps, platform, and infrastructure teams
- Architects evaluating model serving, workload placement, and scaling
- Teams taking chatbots, copilots, voice agents, or other AI workloads into production
If you’re responsible for the performance or infrastructure behind production AI applications, this eBook will help you understand the architecture decisions that influence inference speed and scale.