How LLMs Work: The Hidden Hardware Demands of AI

At their core, large language models (LLMs) perform a seemingly simple task: predicting the most probable next token in a sequence. A token is the fundamental unit of data for an LLM, representing a word or part of a word. Executing this process on a massive scale, however, calls for a complex software architecture that learns from vast datasets of text and code. This approach has unlocked remarkable new capabilities in artificial intelligence, but it has also introduced critical new demands on the systems enabling it. Indeed, as the number of parameters in these models grows from billions to trillions, the corresponding hardware demands increase exponentially.

The power of an LLM starts with its software design, but that same design is the source of a primary physical challenge. The internal mechanics of the model impose hardware demands that can saturate the physical infrastructure of the AI clusters, creating a fundamental paradox. The same algorithm that makes an LLM so powerful is also what creates a physical data traffic jam that the current hardware cannot handle. 

Understanding the hardware requirements of an LLM begins with examining its software process. LLMs are trained on enormous datasets, often consisting of billions of web pages, books and articles, allowing them to learn the statistical relationships between words and phrases. The process of preparing human language for machines begins with tokenization, where text is segmented into smaller units called tokens and assigned numerical IDs.

Each token's numerical ID is then mapped to an embedding, a multi-dimensional vector that captures the semantic meaning of the token. The corresponding embedding tables can be enormous and consume vast amounts of high-speed memory to store and access.

Most modern LLMs are built on the Transformer neural network architecture, a structure designed for parallel processing across thousands of processors. This was a breakthrough compared to older recurrent neural network (RNN) architectures, which could only process data sequentially. This architecture's key component is the self-attention mechanism, a function that weighs the importance of different words in a sequence. The model itself is a deep neural network with billions or even trillions of parameters, which are the internal weights and biases continuously adjusted during training.

The self-attention mechanism creates an N-squared computational problem and generates a substantial data shuffle between processors for every token generated. Together, the memory demands of embeddings and the data traffic from self-attention define the core LLM hardware requirements.

The self-attention mechanism is the source of both an LLM’s power and its immense hardware demands. This function enables the model to understand context, which is crucial for identifying long-range dependencies in text, allowing the model to understand how a word's meaning is influenced by other words that appeared much earlier in a sequence. To accomplish this, the model must compare every token against every other token in its context window for each step of the process. The N-squared computational requirement creates a massive east-west data traffic explosion inside the GPU cluster. This processor-to-processor communication is the single most demanding workload in the entire AI cluster. If the physical interconnects connecting the processors cannot handle this data deluge, the GPUs will sit idle while starved for data, creating a severe performance bottleneck that software alone cannot resolve.

The resulting hardware dilemma presents two primary physical challenges: maintaining signal integrity at high speeds across thousands of parallel connections and achieving the extreme connection density needed to link every processor in the cluster physically. Solving these two challenges is now the primary focus for engineers designing next-generation AI hardware.

Addressing the internal data traffic created by the self-attention mechanism hinges on a new generation of high-speed, high-density interconnects. This demands a system-level approach to the physical layer that confronts the two primary engineering problems born from this intense data traffic: connection density and signal integrity.

The first obstacle is achieving extreme connection density. To minimize latency, AI servers must pack an enormous number of GPUs and accelerators as close together as possible on a single board, often using mezzanine cards to build vertically. This presents a major physical challenge, as thousands of high-speed, parallel connections must be made in an incredibly small space, pushing traditional connector designs to their absolute limits.

Maintaining signal integrity is the second, equally critical issue. At next-generation speeds, routing high-speed signals across the long, lossy traces of a traditional printed circuit board (PCB) significantly degrades them. This signal degradation introduces bit errors and limits the effective bandwidth of the connection. The result is a performance bottleneck that can undermine the processor’s power before the data even leaves the board.

Without robust solutions for both density and signal integrity, the scalability of the AI cluster is fundamentally limited, preventing the training of larger, more powerful models.

The performance of an LLM is ultimately a function of its hardware. While the software architecture defines the task, the physical interconnects determine the speed and efficiency of its execution. Molex applies deep engineering expertise to address the core signal integrity and density challenges created by LLMs.

This expertise is reflected in a portfolio of solutions engineered for the specific demands of AI clusters. To address the density challenge, Mirror Mezz Pro Connectors deliver an ultra-high-density, high-speed board-to-board solution capable of handling next-generation speeds while minimizing space. To overcome the signal integrity challenge, CX2 Dual Speed Connectors and Cable Assemblies provide direct, bypassed connection from the processor to other components, preserving signal integrity and reducing latency. Together, these solutions form a comprehensive on-board physical layer strategy. They manage both density and signal integrity challenges to support the massive internal data flow of an AI cluster, supplying the physical hardware essential to meet LLM hardware requirements and build powerful AI clusters for the modern era. To dive deeper, explore Molex solutions for AI and machine learning infrastructure.