What is LLM inference infrastructure?

LLM inference infrastructure encompasses the systems and workflows needed to run LLM inference reliably and cost-effectively in production. It includes everything from hardware provisioning to software coordination and operational monitoring.

Key components of LLM inference infrastructure include:

• Hardware provisioning: Access to high-performance compute resources like GPUs and TPUs.
• Model deployment: Packaging model weights, runtime configuration, adapters, and serving code so the model can be released, rolled back, and updated safely.
• Serving runtime: Software that loads the model, accepts requests, runs inference, and returns results through APIs or application workflows. Examples include inference servers, model gateways, and framework-specific runtimes.
• Orchestration: Tools that manage resource allocation, scale workloads dynamically, and manage model versions across multiple environments.
• Observability systems: Logging, monitoring, and tracing tools that offer insight into performance metrics such as GPU utilization, latency, throughput, and failure rates.
• Security and access control: Controls that protect sensitive prompts, outputs, model artifacts, and internal data while limiting who can invoke or modify deployed models.
• Cost-to-serve management: Processes for tracking the cost of each workload and tuning model choice, hardware, batching, routing, and scaling policies to keep serving economically sustainable.
• Operational procedures: Standardized workflows and automation that enable teams to deploy updates, manage versions, handle failures, and ensure high availability. As inference demand scales, having repeatable, efficient operations becomes critical to managing growing workloads.