> For the complete documentation index, see [llms.txt](https://handbook.modular.com/llms.txt).
> Markdown versions of all pages are available by appending .md to any URL.

# 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.