A Practical Roadmap to Learning LLM Inference
A Practical Roadmap to Learning LLM Inference (With the Best Learning Resources)
Large Language Models (LLMs) have become the backbone of modern AI applications. While building applications with APIs is relatively straightforward, understanding how LLM inference works under the hood is what separates an LLM user from an LLM engineer.
Over the past few months, I've spent time learning LLM inference. One challenge I encountered was that most tutorials explain only one part of the system—transformers, GPU architecture, optimization techniques, or inference engines—but rarely connect everything together.
To make the learning journey easier, I compiled the most valuable resources I found into a structured roadmap. If you're interested in building faster, more efficient, and production-ready LLM applications, this guide is for you.
1. Learn the Fundamentals of LLM Inference
Before optimizing an LLM, it's important to understand what actually happens when you send a prompt to a model.
Tokenization
LLMs cannot directly understand text. Instead, they convert words or subwords into numerical tokens that the model can process. Different tokenizers may split the same sentence differently, affecting both inference speed and token usage.
Recommended Resources
NVIDIA AI Engineering Talk – Excellent overview of the complete inference pipeline.
Hugging Face: LLM Inference – Covers tokenization with practical examples.
Forward Pass
A forward pass is the computation where the model processes the input tokens through every transformer layer to predict the probability distribution of the next token. During inference, only the forward pass is executed since no model parameters are updated.
Recommended Resources
Hugging Face – LLM Inference
Abi Aryan's blog on LLM inference
Autoregressive Generation
LLMs generate text one token at a time. After predicting one token, it becomes part of the input for predicting the next token. This iterative process is called autoregressive generation.
Recommended Resource
NVIDIA AI Engineering Talk
Prefill Phase
The prefill stage processes the entire input prompt in a single forward pass and constructs the initial Key-Value (KV) cache. This stage largely determines the Time To First Token (TTFT) experienced by users.
Recommended Resources
NVIDIA AI Engineering Talk
Hugging Face – LLM Inference
Decode Phase
Once the KV cache has been created, the model enters the decode stage, generating one token at a time while reusing cached information. Since only a single new token is processed during each iteration, decoding is much more efficient than repeatedly processing the entire prompt.
Recommended Resources
NVIDIA AI Engineering Talk
Abi Aryan's LLM inference blog
KV Cache
The Key-Value (KV) cache stores attention information computed during previous decoding steps. Instead of recomputing attention for every token, the model reuses cached values, dramatically reducing computation and latency.
Recommended Resources
Hugging Face – LLM Inference
NVIDIA AI Engineering Talk
TTFT, ITL, Throughput, and Latency
These are the core performance metrics used to evaluate inference systems.
Time To First Token (TTFT): Time taken before the first output token appears.
Inter-Token Latency (ITL): Delay between consecutive generated tokens.
Latency: Total response time.
Throughput: Number of tokens or requests processed per second.
Understanding these metrics helps explain why two inference systems can produce identical outputs but differ significantly in responsiveness.
Recommended Resources
NVIDIA AI Engineering Talk
Abi Aryan's LLM inference blog
2. Understand the Transformer Components Used During Inference
You don't need to master every detail of transformer architectures, but you should understand the components directly involved in inference.
Transformer Blocks
A transformer consists of multiple identical blocks stacked together. Each block performs attention calculations followed by feed-forward computations to refine token representations.
Embeddings
Embeddings convert discrete token IDs into dense numerical vectors that capture semantic meaning before they enter the transformer.
Self-Attention
Self-attention enables each token to attend to other relevant tokens in the sequence, allowing the model to capture long-range dependencies and contextual relationships.
Query (Q), Key (K), and Value (V)
Self-attention relies on three vectors:
Query: What the current token is looking for.
Key: What information each token contains.
Value: The actual information exchanged between tokens.
Understanding Q, K, and V makes concepts like KV Cache, FlashAttention, and PagedAttention much easier to grasp later.
Recommended Resource
LLM Visualizer by Brendan Bycroft
https://bbycroft.net/llm
This interactive visualization is one of the best tools for building intuition about transformer inference.
3. Learn GPU & Hardware Fundamentals
LLM inference performance is often limited by hardware rather than model architecture.
GPU Architecture
Modern GPUs consist of thousands of lightweight cores organized into Streaming Multiprocessors (SMs) that execute tensor operations in parallel.
Memory Hierarchy
Understanding HBM (High Bandwidth Memory), SRAM, memory bandwidth, and cache hierarchy explains why many inference workloads are memory-bound rather than compute-bound.
FLOPS
Floating Point Operations Per Second (FLOPS) measure the theoretical computational capability of hardware, but high FLOPS alone do not guarantee fast inference.
Compute-Bound vs. Memory-Bound Workloads
Some inference operations are limited by arithmetic computation, while others spend most of their time waiting for memory access. Many optimization techniques focus on reducing memory bottlenecks.
Recommended Resources
Making Deep Learning Go Brrrr
Basic Facts About GPUs
4. Learn Modern Inference Optimization Techniques
Once you understand the fundamentals, it's time to learn how production inference systems achieve high performance.
Quantization
Reduces numerical precision (e.g., FP16 to INT8 or INT4), lowering memory usage while improving inference speed with minimal accuracy loss.
FlashAttention
A memory-efficient attention algorithm that minimizes memory access and accelerates transformer attention computation.
PagedAttention
Efficiently manages KV cache memory, enabling large numbers of concurrent inference requests without excessive memory fragmentation.
Chunked Prefill
Processes long prompts in smaller chunks, improving GPU utilization and reducing latency.
Speculative Decoding
Uses a smaller draft model to predict multiple tokens before verification by a larger model, increasing decoding speed.
Prompt Caching
Caches repeated prompt computations, avoiding redundant processing for identical or similar requests.
Continuous Batching
Dynamically batches incoming requests to maximize GPU utilization while maintaining low latency.
Recommended Resources
NVIDIA – Mastering LLM Techniques
Original research papers for FlashAttention, PagedAttention, Quantization, and Speculative Decoding
5. Explore Modern Inference Engines
Finally, explore real-world inference engines that implement many of the techniques above.
vLLM
Widely regarded as the best general-purpose inference engine for high-throughput production deployments. It pioneered PagedAttention, enabling efficient KV cache management.
Recommended Resource
Inside vLLM by Aleksa Gordić
SGLang
Designed for applications where requests share context, making it particularly effective for multi-turn conversations, RAG pipelines, and AI agents.
llama.cpp
The go-to inference engine for local deployment on CPUs and edge devices. Its lightweight design makes it ideal for experimentation without requiring powerful GPUs.
Recommended Resource
Distributed LLM Inference
My Recommended Learning Order
If I were starting from scratch today, I would follow this sequence:
Learn the fundamentals of LLM inference.
Understand the transformer components used during inference.
Build a solid understanding of GPU architecture.
Study modern inference optimization techniques.
Experiment with an inference engine such as vLLM, SGLang, or llama.cpp.
Build small projects to reinforce your understanding.
For mid-level and senior ML/AI engineers, the next step is exploring parallelism, distributed inference, and multi-GPU serving, which are essential for deploying large-scale LLM systems.
Final Thoughts
LLM inference lies at the intersection of deep learning, systems engineering, and computer architecture. Although the topic can seem intimidating at first, breaking it into these five stages makes the learning process much more approachable.
Rather than memorizing every optimization technique, focus first on understanding why they exist. Once you grasp the fundamentals—how transformers work, why GPUs matter, and where inference bottlenecks occur—modern techniques like FlashAttention, PagedAttention, and speculative decoding become much easier to understand.
I hope this roadmap helps you navigate the world of LLM inference more efficiently.
Happy learning!
