AI Inference Study Notes: Deep Dive into the Internal Mechanisms of Large Language Model Inference

Original Author & Source

• Original Author/Maintainer: HAN-oQo
• Source Platform: GitHub
• Original Project Name: HAN-oQo.github.io
• Original Link: https://github.com/HAN-oQo/HAN-oQo.github.io
• Publication Date: 2026-06-10

Core Content Overview

This collection of study notes focuses on the internal mechanisms of large language model (LLM) inference, covering key concepts, optimization techniques, and implementation details. It is suitable for developers who want to deeply understand the inference process. The inference stage is a critical link that determines the user experience of LLMs; understanding its mechanisms is of great value for optimizing deployment, designing architectures, and more.

In LLM development and applications, training determines the upper limit of capabilities, while inference is the core of user experience. Understanding inference mechanisms is crucial for the following groups:

• AI Engineers: Optimize model deployment and reduce inference costs
• System Architects: Design efficient inference service architectures
• Application Developers: Better utilize LLM APIs and write efficient prompts
• Researchers: Explore new inference optimization methods

Autoregressive Generation

LLM text generation uses an autoregressive approach: generate one token at a time, add it to the input sequence, and continue generating until the end or the maximum length is reached. It consists of two stages:

1. Prefill Stage: Process the input prompt, compute and cache the KV cache
2. Decode Stage: Generate tokens one by one, access and update the KV cache

KV Cache Mechanism

In the Transformer attention layer, the Key and Value vectors of already generated tokens can be cached to avoid repeated calculations, significantly accelerating the decode stage.

Attention Calculation

The complexity of standard self-attention is O(n²), where n is the sequence length. Inference costs increase significantly for long sequences.

Quantization

Convert model weights from high precision to low precision (e.g., INT8/INT4) to reduce memory usage, speed up computation, and lower energy consumption. Common methods:

• Post-Training Quantization (PTQ)
• Quantization-Aware Training (QAT)
• LLM-specific algorithms like GPTQ and AWQ

Speculative Decoding

Quickly generate candidate tokens via a small draft model, then have the large model verify them in parallel. Accept the passed tokens to accelerate generation without losing quality.

Continuous Batching

Dynamically add new requests to improve GPU throughput and resource utilization, solving the waiting problem in traditional batching.

Paged Attention

Drawing on the idea of virtual memory, manage KV cache with paging to solve the inflexible memory allocation problem, supporting efficient sharing and longer contexts.

Latency vs Throughput

• Interactive Applications (e.g., chatbots): Prioritize first-token latency and streaming output
• Batch Processing Applications (e.g., document analysis): Prioritize overall throughput

Memory Management

Need to handle high GPU memory demands. Strategies include reasonable batch size, KV cache compression and eviction, model sharding, and pipeline parallelism.

Service Scheduling

Production environments need to handle concurrent requests, considering request priority, context length differences, fairness, and resource allocation.

This note points to important learning directions. For those who want to dive deeper, it is recommended to focus on:

• Classic Papers: Attention Is All You Need, GPTQ, PagedAttention, etc.
• Open-Source Implementations: Inference frameworks like vLLM, TensorRT-LLM, llama.cpp
• Hardware Optimization: Hardware acceleration support for GPUs, TPUs, etc.
• Cutting-Edge Research: Track the latest progress in the field of inference optimization

LLM inference is a comprehensive technical field involving algorithms, systems, and hardware. With the widespread application of LLMs, inference optimization has become the key to reducing deployment costs and improving user experience.

For developers and researchers, a deep understanding of inference mechanisms helps make better technical decisions: choosing inference frameworks, optimizing service architectures, or designing efficient prompt strategies.