LLM Infrastructure Explained for SREs: From Request to GPU

Follow one request end to end

Client
  ↓
Load balancer / AI gateway
  ↓
AI application
  ↓
Model server + scheduler
  ↓
GPU memory + compute
  ↓
Tokens streamed back

Telemetry crosses the whole path.

The gateway and application layer

An AI Gateway can handle routing, quotas, authentication and provider policy. The application may add Retrieval-Augmented Generation (RAG), tools or an agent loop before or between model calls, so the final dependency graph can be much larger than one model endpoint.

The model-serving layer

Model Serving loads model weights, accepts inference requests and schedules work. Engines may use Continuous Batching, cache management and request scheduling to increase GPU utilization while trying to preserve latency targets.

The GPU layer

GPU compute and VRAM are the critical resources for self-hosted models. Model weights consume memory, and active inference state such as KV Cache adds dynamic memory pressure. Quantization can reduce memory requirements, with trade-offs that should be validated for the use case.

Reliability architecture

Use familiar patterns: health checks, High Availability (HA), Circuit Breaker, routing and capacity limits. But define health at multiple layers: endpoint reachable, model loaded, GPU available, provider quota healthy and application quality acceptable.

What an AI SRE ultimately owns

The job is to make the full request path predictable: bounded latency, controlled concurrency, known failure modes, observable decisions, safe tool access, measurable quality and sustainable cost.