> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/llm-inference-handbook/infrastructure-and-operations/what-is-llm-inference-infrastructure.md).

# 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.
* **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.
* **Operational procedures**: Standardized workflows and automation that enable teams to deploy updates, enforce access control, handle failures, and ensure high availability. As inference demand scales, having repeatable, efficient operations becomes critical to managing growing workloads.


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