Is the LLM Inference & Large-Scale Deployment certificate verifiable?

Yes. Every issued Hootix Academy certificate carries a unique credential code that anyone can verify online.

How is the LLM Inference & Large-Scale Deployment exam structured?

It is a 105-minute proctored multiple-choice exam of 75 questions; you need 75% to pass.

Do I need to buy the course to take the exam?

You can purchase the certification exam on its own, or bundle it with the full study course at a reduced price.

How long does the LLM Inference & Large-Scale Deployment course take?

About 38 hours of self-paced study.

LLM Inference & Large-Scale Deployment Certification

Serve large language models fast, cheap, and reliably at scale

Key facts

Level: Professional
Field: Artificial Intelligence & LLMs
Estimated study time: about 38 hours
Credential price: $149
Exam: 75 questions · 105 minutes · pass mark 75%

About the LLM Inference & Large-Scale Deployment certification

Turn a trained model into a production service that is fast, affordable, and reliable under real traffic. This professional certification covers how inference differs fundamentally from training, the autoregressive decode loop, and the two phases that dominate cost: compute-bound prefill and memory-bound decode. You will master the KV-cache — why it exists, what it costs in GPU memory, and why it, not the weights, often limits throughput — and the batching strategies that amortize that cost: static, dynamic, and continuous (in-flight) batching. The core serving toolkit follows: PagedAttention and vLLM for memory-efficient KV management, FlashAttention for IO-aware attention, quantization (int8/int4, GPTQ, AWQ, GGUF, weight-only vs weight-and-activation) and its accuracy tradeoffs, speculative/assisted decoding for latency, and tensor parallelism for models too large for one GPU. You will do the GPU-memory math (parameters, KV-cache, activations), reason about the latency metrics that matter — TTFT, TPOT/ITL, and end-to-end — and navigate the throughput-vs-latency tradeoff. Finally you will operate the service: autoscaling and load balancing, choosing a serving stack (vLLM / TGI / TensorRT-LLM), prompt and prefix caching, cost optimization, monitoring and observability, and serving-time guardrails. By the end you can size, deploy, tune, and defend a real LLM inference deployment.

What you will learn

The official LLM Inference & Large-Scale Deployment study course covers:

From Training to Inference — Why serving is a different problem: the forward-only decode loop, the prefill/decode split, and the KV-cache that defines LLM inference.
The KV-Cache & GPU-Memory Math — Why the KV-cache exists, what it costs, and how to size weights + KV + activations for a real deployment.
Batching & Attention Kernels — Static, dynamic, and continuous batching; PagedAttention's paged KV; and FlashAttention's IO-aware exact attention.
Quantization & Faster Decoding — Int8/int4 weight and activation quantization (GPTQ, AWQ, GGUF), the accuracy tradeoff, and speculative decoding.
Serving at Scale — Latency metrics, throughput/latency tradeoffs, tensor parallelism, serving stacks, caching, autoscaling, cost, observability, and guardrails.

Prerequisites

LLM Training & Fine-Tuning

Frequently asked questions

Is the LLM Inference & Large-Scale Deployment certificate verifiable?: Yes. Every issued Hootix Academy certificate carries a unique credential code that anyone can verify online.
How is the LLM Inference & Large-Scale Deployment exam structured?: It is a 105-minute proctored multiple-choice exam of 75 questions; you need 75% to pass.
Do I need to buy the course to take the exam?: You can purchase the certification exam on its own, or bundle it with the full study course at a reduced price.
How long does the LLM Inference & Large-Scale Deployment course take?: About 38 hours of self-paced study.