BC Skills Academy

LLM Inference & Large-Scale Deployment

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

Key facts

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.

Course outline

The LLM Inference & Large-Scale Deployment — Study Guide course covers:

  1. 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. (3 lessons)
  2. 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. (3 lessons)
  3. Batching & Attention Kernels — Static, dynamic, and continuous batching; PagedAttention's paged KV; and FlashAttention's IO-aware exact attention. (3 lessons)
  4. Quantization & Faster Decoding — Int8/int4 weight and activation quantization (GPTQ, AWQ, GGUF), the accuracy tradeoff, and speculative decoding. (3 lessons)
  5. Serving at Scale — Latency metrics, throughput/latency tradeoffs, tensor parallelism, serving stacks, caching, autoscaling, cost, observability, and guardrails. (4 lessons)

Prerequisites

Frequently asked questions

Is the LLM Inference & Large-Scale Deployment certificate verifiable?
Yes. Every certificate issued by BC Skills Academy carries a unique credential ID that anyone can check on the public verification page.
How is the LLM Inference & Large-Scale Deployment exam structured?
It is a timed, randomized multiple-choice exam of 75 questions in 105 minutes, graded instantly; you need 75% to pass.
Can I get a refund?
Full refund within 7 days of purchase if you have not started the exam. Once the exam has been started or completed, the purchase is no longer refundable.

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