Best LLM inference engineering courses and resources in 2026

Published by Fanout, which is one of the resources discussed below. No provider paid for inclusion. Entries are unranked and ordered alphabetically by brand. We compared public provider information and last verified it on August 6, 2026. Prices, schedules, and access can change.

Comparison criteria: Inference-systems depth, Hands-on code or projects, Serving-runtime breadth, Production metrics and capacity planning, Prerequisites and access, Format and update recency.

  • Abi Aryan: AI Inference Engineering & Systems Design

    Format: Live senior-level cohort

    Best for: Senior engineers and technical leaders focused on accelerator economics, inference gateways, architecture, benchmarking, and observability.

    Tradeoff: This is a premium cohort with limited scheduled access rather than an always-available self-paced course.

    View Abi Aryan resource

  • DeepLearning.AI: Fast & Efficient LLM Inference with vLLM

    Format: Short self-paced course

    Best for: A concise, hands-on introduction to model compression, vLLM serving, continuous batching, PagedAttention, prefix caching, load testing, and evaluation.

    Tradeoff: Its short duration and vLLM-centered workflow make it a focused introduction rather than a complete distributed-inference curriculum.

    View DeepLearning.AI resource

  • Fanout: Inference Engineering

    Format: Self-paced course with free and Pro lessons

    Best for: Engineers who want a growing systems-first path with paper explanations, capacity calculators, and links into AI research, ML mathematics, and system design.

    Tradeoff: Practice and Code are being refined; interview questions are coming soon.

    View Fanout resource

  • Full Stack Deep Learning: Full Stack LLM Bootcamp

    Format: Free recorded bootcamp

    Best for: Engineers who need the wider production context around LLM applications, deployment, monitoring, LLMOps, architecture, and user experience.

    Tradeoff: The 2023 material is broader and older than a dedicated modern inference-serving curriculum.

    View Full Stack Deep Learning resource

  • Hugging Face: LLM Course inference sections

    Format: Free open course

    Best for: A free reference for prefill, decode, runtime selection, optimized deployment, and comparisons among TGI, vLLM, and llama.cpp.

    Tradeoff: Inference is one part of a broader LLM course, without the projects and benchmark depth of a dedicated serving program.

    View Hugging Face resource

  • NVIDIA: Sizing LLM Inference Systems

    Format: Vendor training and guides

    Best for: Teams already using NVIDIA infrastructure that need practical benchmarking, deployment sizing, latency constraints, capacity, and total-cost analysis.

    Tradeoff: The workflow is designed around NVIDIA infrastructure, so pair it with open runtime documentation when vendor-neutral comparison matters.

    View NVIDIA resource

  • Stanford University: CS349D: AI Inference Infrastructure

    Format: University systems course

    Best for: Advanced systems learners who want a research-paper-driven syllabus and a serving-engine project spanning parallelism, batching, caching, and disaggregation.

    Tradeoff: The public syllabus and readings are useful, but the complete course experience is tied to Stanford enrollment.

    View Stanford University resource

  • Vizuara: LLM Inference Engineering: Theory, Practicals and Research

    Format: Live cohort

    Best for: Engineers seeking substantial live instruction, hardware labs, edge deployment, serving-runtime breadth, and optional research mentorship.

    Tradeoff: Schedules and outcomes are provider claims on a cohort sales page, so confirm current availability and terms directly.

    View Vizuara resource