Requirements for an Inference Engine
Understand the essential functional and nonfunctional requirements for inference engines in generative AI systems. Learn how latency metrics like time to first token and token pacing impact user experience. Discover GPU memory challenges with KV cache management, scheduling strategies, batching techniques, and model parallelism. Gain insights into hardware choices and deployment trade-offs that influence performance and cost. This lesson equips you to design inference engines that meet operational guarantees essential for production-grade generative AI applications.
In enterprise System Design, model serving is the operational bridge where assembled prompts turn into live output streams. Traditional web microservices scale horizontally by adding stateless containers that handle independent, millisecond requests. In contrast, LLM serving is stateful, hardware-constrained, and memory-bandwidth bound.
Deploying generative models in production requires managing prefill and decode, allocating GPU memory for the KV cache, and scheduling requests to improve hardware utilization. This lesson explores inference performance, continuous batching, latency SLOs, and distributed model parallelism, along with the trade-offs each introduces.
Why inference requirements matter
In an interview, requirements are the fastest way to turn a vague “design an LLM serving system” problem statement into an architecture discussion with measurable targets. In production, the same requirements govern admission control, hardware sizing, and pricing. The request path begins when a client submits a prompt and ends only after streamed tokens arrive with a cadence users can tolerate.
Two latency metrics dominate interactive systems. TTFT measures time to first token, which is the delay between request acceptance and the first visible output. TPOT measures time per output token, which is the steady-state speed of generation after streaming starts. A response with acceptable total runtime can still feel bad if TTFT is high or if token cadence stutters.
Note: Average latency hides the user experience. A chat product usually fails at the tail, not at the mean.
The serving tier must also satisfy broader constraints during that same life cycle.
Throughput: The system must sustain enough aggregate token generation under peak load.
Availability: The service must continue routing and streaming ...
These requirements now split naturally into what the system ...