TensorRT-LLM — Fundamentals, Integration and Guided Practice

Understand the problems TensorRT-LLM addresses, its building blocks and the limits of AI systems built with it.

• Core concepts and vocabulary
• Where TensorRT-LLM fits in an AI system
• Known limitations and risks
• Choosing TensorRT-LLM for a workflow

Set up TensorRT-LLM, handle credentials safely and produce a first working result.

• Installation or account setup
• Keys, secrets and data access
• A first end-to-end example
• Reading outputs critically

Learn the main APIs and the patterns practitioners use with TensorRT-LLM.

• Main APIs and configuration
• Inputs, outputs and data formats
• Structured outputs or pipelines
• Errors and retries

Feed the right data in and keep sensitive data protected.

• Preparing and cleaning data
• Retrieval or feature preparation
• Privacy and access boundaries
• Versioning data and prompts or models

Measure whether outputs are good enough, using clear criteria.

• Test sets and criteria
• Automated and human review
• Comparing versions
• Tracking regressions

Make the workflow affordable, fast enough and safe to run.

• Cost and latency measurement
• Caching and batching
• Monitoring and tracing
• Guardrails and human review

Apply TensorRT-LLM to one real workflow and show evidence that it works.

• Scope and success measure
• Build
• Evaluation report
• Limitations and risks

Consolidate responsible-use practices and plan improvements.

• Risk and limitation review
• Maintenance as models and libraries change
• Documentation for users
• Next steps