Inference Engineering Academy
About the academy
Practical education in building, understanding, and operating model inference systems.
Who maintains this platform?
Sandipan Kundu maintains Inference Engineering Academy and authors its technical guides. The curriculum covers Foundations, CUDA, PyTorch, Transformers, vLLM, SGLang, and NVIDIA Dynamo.
How to assess our technical content
- Courses identify their target software release. Check that release before copying commands into a different environment.
- Articles link primary documentation and research so readers can inspect the evidence.
- Directory entries distinguish documented support, conditional support, and missing evidence. Unknown support does not mean unsupported.
- Calculated memory requirements are estimates with explicit assumptions. They are separate from measured GPU performance.
- Published benchmark comparisons attribute their results to the original authors. A suggested experiment is not a claim that we ran it.
Report a correction with the page URL, software version, observed behavior, and a primary source or reproduction. Email contact@inference-engineering.xyz.
Start learning
Read the free Foundations course, browse coding problems, or estimate GPU memory.