Last checked
Best LLM Inference Engineering Course: What Each One Teaches
The short answer
For inference as a speciality (KV cache, batching, quantization, speculative decoding, parallelism, kernels), Fanout's Inference Engineering course was the deepest self-paced option we found on 4 November 2026, and Vizuara's live cohort on Maven was the most detailed live one. If you want inference as part of the whole AI engineering job, our AI Engineering course teaches it in 27 lessons inside a 214-lesson course, for ₹1,333 once in India or $35 elsewhere. For free, read the vLLM documentation and the public slides of Harvard's CS2680.
Our inference track, inside the Generative AI and Agentic AI Engineering course: 27 inference lessons in a 214-lesson course, ₹1,333 once in India or $35 elsewhere, lifetime access.
Written by Roni Das, who makes systemdesign.academy, so this is not an independent review. Every fact about another provider was read on that provider's own page on 4 November 2026, and each row links to its source. There are no affiliate links.
LLM inference courses, side by side
Each row was read on the provider's own page on 4 November 2026. Dollar prices are what a US visitor saw; rupee prices what a visitor in India saw.
| Option | Price (checked 4 November 2026) | What it covers | Format | Source |
|---|---|---|---|---|
| systemdesign.academy AI Engineering, inference track (ours) | ₹1,333 once in India or $35 elsewhere, for the whole course. Lifetime access. | 27 lessons: How Models Generate (12), Serving and Inference Basics (12), LLM and GenAI Ops (3). Prefill and decode, TTFT and tokens per second, the KV cache, continuous batching and PagedAttention, quantization, speculative decoding, prompt caching, serving runtimes, cost per token, model routing | Written lessons with diagrams and quizzes, and an AI tutor beside every lesson. No videos | /inference-engineer |
| Fanout: Inference Engineering Course | Introductory lessons free; a Pro plan opens the full course and the rest of Fanout's catalogue. US: $39 a month, $85 a quarter, $158.90 for the first year with code LAUNCH30 (then $227 a year), or $179.20 once for lifetime (30% off $256). India: ₹1,700 a month, ₹3,600 a quarter, ₹6,510 once for lifetime (30% off ₹9,300). | KV cache, continuous batching, PagedAttention, prefix caching, chunked prefill, quantization (FP8, INT8, AWQ, GPTQ), vLLM vs SGLang, TensorRT-LLM, speculative decoding, tensor and pipeline parallelism, Triton and FlashAttention, TTFT and TPOT benchmarking | Self-paced explanations, videos and exercises, with labs and capstones | fanout.sh/inference-eng |
| Vizuara on Maven: LLM Inference Engineering: Theory, Practicals and Research | The page did not show a price or the next public cohort date on 4 November 2026. It offers private cohorts for teams. | 45 hours of live lectures, 4 hardware labs (llama.cpp, MLX, Raspberry Pi, Jetson, Android), 9 guest lectures, two months of 1:1 research mentorship; vLLM, SGLang, TensorRT-LLM, quantization, FlashAttention, speculative decoding | Live cohort, 9 AM IST on weekdays, recorded; the page says 15 to 20 hours a week | maven.com/vizuara/inference-workshop |
| Harvard CS2680: Modern AI Systems: Agents and System Optimizations | A Harvard class for its enrolled students. The schedule, slides and reading lists are public on the course site. | Fall 2026, taught by Juncheng Yang. Part II covers LLM serving: GPUs, batching, paging and parallelism, speculative decoding, KV cache optimization, quantization, prefix caching and routing | University lectures with assignments and a final project | cs2680.seas.harvard.edu |
| vLLM documentation | Free. | The official user guide, benchmarking guide and developer guide for the vLLM serving engine | Documentation | docs.vllm.ai |
Fanout's page loads its prices in the browser; the figures above are what the rendered page showed. Vizuara's page showed no price that day. Check each source before you buy.
What an inference engineering course should teach
- Why generation is slow: prefill against decode, and why decode is limited by memory reads, not maths.
- The KV cache: how much memory one request needs (2 x layers x KV heads x head size x bytes, per token) and why it sets your batch size.
- Batching: static against continuous batching, PagedAttention, prefix caching and chunked prefill.
- Making the model smaller and faster: quantization (FP8, INT8, 4-bit) and speculative decoding.
- Serving engines: vLLM, SGLang, TensorRT-LLM and llama.cpp, and how to choose between them.
- Measuring it: time to first token, time per output token, throughput, tail latency and cost per million tokens.
- Scaling out: tensor, pipeline and expert parallelism, and routing requests across replicas.
Where the others are stronger
- Fanout goes deeper on inference itself than we do: kernels with Triton and FlashAttention, CUDA graphs, Nsight profiling, multi-LoRA serving and disaggregated prefill.
- Vizuara's cohort is live, with hardware labs on llama.cpp, MLX, a Raspberry Pi, a Jetson and Android phones, and a research mentorship track.
- Harvard's CS2680 slides and reading lists are free and current, and they pair serving with agent systems.
- The vLLM documentation is the primary source for vLLM itself.
Where ours falls short
- Our inference track is 27 lessons inside a wider course, not a stand-alone inference course. We do not teach GPU kernels.
- Our lessons are written. There are no videos and no live teacher in the self-paced course.
If you want to serve a model yourself, live
One class of our live programme, AI Engineering Live, serves an open model with vLLM's OpenAI-compatible server, load-tests it, and works out the KV cache memory a request needs. It runs on 14 Saturdays from 7 November 2026, 8:30 PM to 10:30 PM IST, for a fee of ₹15,000 in India or $300 elsewhere.
For interviews, the Inference Engineering interview pack (₹999 or $20) has 120 questions with worked answers, and the free LLM inference system design walkthrough designs a ChatGPT-scale serving system end to end.
Free places to start
Read these first. Most of the track is paid, and these show how it teaches.
- Design an LLM inference system (free walkthrough): KV cache, batching, routing and failure at ChatGPT scale
- Model serving and inference APIs (free lesson)
- Reading a model name (free lesson): what 27B, FP8 and A3B mean
- LLM Inference Optimization: the course's main inference lesson
- Inference engineer: the role and the full track
- AI Engineering course
- Inference Engineering interview pack
- AI Engineering Live
Questions
What is the best LLM inference engineering course?
For inference as a speciality, Fanout's Inference Engineering course was the deepest self-paced option we found on 4 November 2026, and Vizuara's live cohort on Maven was the most detailed live one. For inference as part of AI engineering, our course teaches it in 27 lessons for ₹1,333 once in India or $35 elsewhere. We publish this page, so weigh our view with that in mind.
Is there a free LLM inference course?
Yes, in pieces. The vLLM documentation is free, Harvard's CS2680 publishes its slides and reading lists, and Fanout's introductory lessons are free. Our free LLM inference system design walkthrough is a good test of what you already know.
What does an inference engineer do?
An inference engineer makes trained models answer fast and cheaply: choosing the serving engine, sizing GPUs and the KV cache, batching requests, quantizing the model and measuring latency and cost per token.
Do I need a GPU to learn inference engineering?
Not to start. Our lessons and the free docs need none. To serve a model yourself you need a GPU for a few hours, which you can rent; our live programme shares a recorded run for those who do not rent one.
How much do inference courses cost?
On 4 November 2026: free for the vLLM docs and Harvard's slides; Fanout from $39 a month or $179.20 once for lifetime as a US visitor saw it; ours ₹1,333 once in India or $35 elsewhere for the whole AI Engineering course. Vizuara's page did not show a price that day.
Who wrote this page, and is it independent?
Roni Das, who makes systemdesign.academy, wrote it. It is not independent: one of the options is ours. There are no affiliate links. Every other provider's fact was read on its own page on 4 November 2026.
Other questions we answer the same way
- What is the best system design course in India?
- Which AI engineering bootcamp runs live online in India?
- How do I prepare for a system design interview with mock interviews?
- What is the best agentic AI course with projects (RAG, agents, evals)?
- What is the best system design course in 2026?
- What is the best AI engineering course?
Interview soon?
Get all 120 questions in the Inference Engineering pack
Each question has why it is asked, a full answer, what the interviewer listens for, the follow-ups, the common mistakes and a diagram (141 diagrams in all). A PDF you keep, junior to staff level.
India: UPI, cards or netbanking through Razorpay. Elsewhere: card or PayPal. Download at once from My packs.