Understanding GPU Clusters for Large Language Models: An Investor’s Guide

Introduction
In recent years, the boom in artificial intelligence – especially generative AI models like large language models (LLMs) – has created a pressing need for powerful computational infrastructure nebius.com. Training or even fine-tuning an advanced AI model on a single graphics processing unit (GPU) is impractical due to the immense computational requirements of these systems nebius.com. Instead, companies rely on GPU clusters (many GPUs working in parallel) to handle the heavy workloads. This white paper provides a foundational overview of the core technologies and concepts behind hosting and operating LLMs on GPU-based infrastructure. It is written for readers with little prior technical knowledge, with the goal of demystifying terms and explaining the basic science in clear, accessible language.

We will begin by explaining what GPU clusters are and why they are the backbone of AI cloud infrastructure. Next, we introduce GPUs themselves – how they differ from regular CPUs and why they excel at AI tasks through parallel processing. We’ll clarify key concepts like VRAM (video memory), tensor operations, and how deep learning models (especially transformers used in LLMs) work under the hood. Throughout the paper, we use visual metaphors and simple analogies to illustrate complex ideas. A glossary of key terms is included at the end for quick reference. By the conclusion, an investor should feel conversant in AI infrastructure fundamentals and better prepared to evaluate opportunities in the AI cloud sector.

GPU Clusters: The Backbone of AI Infrastructure

A rack-mounted GPU cluster on display. Each unit in the rack is an AI server (such as NVIDIA’s DGX systems) containing multiple GPUs, supported by networking (top) and high-speed storage (middle). These interconnected servers work together as one system to train or run large AI models.

A GPU cluster is essentially a network of computers where each machine is equipped with one or more GPUs en.wikipedia.org. In a GPU cluster, many GPUs work in unison on the same task, orchestrated by software to divide and conquer the workload. This differs from a regular single-computer setup in both scale and design: a typical PC might contain a single GPU (or none at all), whereas a GPU cluster can comprise dozens, hundreds, or even thousands of GPUs spread across multiple machines nebius.com. The cluster’s computers (often called nodes) are interconnected with high-speed networks (such as InfiniBand or advanced Ethernet) to share data quickly en.wikipedia.org, so that all GPUs can effectively collaborate on the problem at hand.

Why GPU clusters? The short answer is performance and scale. Large AI models require an enormous number of calculations. By harnessing many GPUs in parallel, a cluster can perform calculations much faster than any single machine. In fact, without GPU clusters, training modern large language models “at such scale would be impossible” nebius.com nebius.com. For perspective, training the famous GPT-3 model (with 175 billion parameters) on a single high-end GPU was estimated to take over 350 years, whereas using a well-equipped GPU cluster (for example, 1,024 GPUs working together) could complete the same task in about a month chatbotkit.com. In practice, AI labs use massive clusters to bring training times down to manageable lengths (weeks or months instead of centuries) chatbotkit.com.

GPU clusters are not only crucial for training LLMs, but also for inference – that is, running the trained model to generate outputs or predictions. During inference, having multiple GPUs allows a system to handle many user queries at once and provide results with low latency. For instance, an online AI service (like a chatbot or cloud API) may distribute incoming requests across a cluster of GPUs to serve many customers simultaneously. The cluster also provides reliability (if one GPU or node fails, others can pick up the load) and flexibility (the workload can be scaled across more GPUs as demand grows) nebius.com. In summary, GPU clusters are the foundational infrastructure that makes it feasible to train huge models and deploy them to users at scale.

Parallel Computing and Task Division

The magic of GPU clusters lies in parallel computing. Instead of one processor working on a task sequentially, we have many processors tackling pieces of the task at the same time. A helpful analogy is to imagine a large project, like assembling a huge puzzle: a single CPU is like one expert working carefully on the puzzle, while a GPU cluster is like an entire team working on different sections of the puzzle simultaneously. By splitting a big job into smaller subtasks, a cluster can solve problems much faster than a lone computer nebius.com. This parallel approach is necessary for LLMs because the datasets and models are so large (often terabytes of data and hundreds of billions of parameters) that no single processor — and even no single GPU — can handle them efficiently alone nebius.com nebius.com.

To use a GPU cluster effectively, specialized software frameworks divide the neural network computations across the GPUs. For example, one common approach is data parallelism, where each GPU gets a different chunk of the data but runs the same model (like giving each team member different puzzle pieces to work on). Another approach is model parallelism, where the model itself is split among GPUs (as if each team member is responsible for a different section of the overall puzzle image). In practice, large-scale training will combine these techniques – breaking up both data and model – to utilize dozens or thousands of GPUs at once without running out of memory on any single one. The end result is a coordinated effort: GPUs communicate intermediate results over the cluster’s high-speed interconnect so that the full task comes together seamlessly, just as puzzle sections assembled by individuals are joined into a complete picture.

GPUs and Parallel Processing in AI

A Graphics Processing Unit (GPU) is a specialized processor originally designed to render graphics and images. Unlike a CPU (central processing unit), which might have a handful of powerful cores optimized for general-purpose computing, a GPU contains hundreds or even thousands of smaller cores optimized for doing many operations at the same time intel.com. This makes GPUs extraordinarily good at parallel processing – performing the same calculation on lots of pieces of data simultaneously. In the context of AI and deep learning, this capability is a perfect match. Training or running a neural network involves operations on large matrices and multi-dimensional arrays of numbers (called tensors). GPUs shine at exactly this kind of math: they can apply mathematical operations (like additions and multiplications) across big tables of numbers in one go, whereas a CPU would have to work through them more sequentially weka.io weka.io.

To illustrate the difference, consider an analogy with painting a mural. A CPU is like a single highly skilled painter who paints one part of the mural at a time with great precision. In contrast, a GPU is like having a team of 1,000 painters each given a small section of the wall – the team can cover a huge area quickly by working in parallel. The GPU’s many cores might individually be slower or less sophisticated than the CPU’s core, but collectively they achieve massive throughput. In technical terms, a GPU’s strength is doing a relatively limited set of operations on very large batches of data all at once weka.io. Early GPUs were built for graphics (where you might color millions of pixels in parallel), but engineers realized the same hardware is ideal for the repetitive linear algebra in machine learning weka.io. Modern GPUs, especially those from NVIDIA, even include special units called Tensor Cores that accelerate matrix and tensor computations used in deep learning digitalocean.com. This hardware acceleration is one reason why GPUs can train AI models much faster than traditional processors – in some cases speedups of 10x or more are achieved by using GPUs for deep learning tasks instead of CPUs.

Another important aspect is how GPUs handle task switching. A CPU can do many different things and switch between tasks (running your operating system, then your web browser, then a spreadsheet calculation) extremely fast. But this versatility comes at a cost: for heavy numerical workloads, the CPU’s context-switching and general-purpose design make it less efficient. GPUs, on the other hand, are more limited in what they do – primarily crunch numbers in parallel – but they do not need to keep switching contexts for general-purpose tasks weka.io weka.io. Think of the CPU as a multitasker juggling many roles, while the GPU is a savant focused on one kind of task (calculating numbers) with full dedication. In AI workloads, that focus pays off in raw performance.

VRAM: High-Speed Memory for GPUs

When discussing GPUs, we often encounter the term VRAM, which stands for Video Random Access Memory. VRAM is the dedicated onboard memory of a GPU – essentially, it’s the GPU’s private workspace for data. This memory is physically located on the graphics card, very close to the GPU cores, and it’s engineered for high bandwidth (meaning it can move a lot of data to and from the GPU very quickly) blog.runpod.io. In simple terms, if the GPU cores are the workers, VRAM is like their readily accessible supply room or workbench where all the necessary materials (data and model parameters) are kept handy. Because VRAM is dedicated to the GPU and extremely fast, it ensures that the thousands of GPU cores stay fed with data and do not sit idle waiting for information blog.runpod.io. By contrast, a CPU uses the system’s main RAM, which is slower and also shared with other components, but a GPU’s VRAM is exclusively for that GPU’s use blog.runpod.io.

For AI tasks, especially training large models, VRAM is critically important. An LLM or any deep learning model consists of millions or billions of parameters (numbers) that define the model. These parameters, along with the input data and intermediate results (called activations), must reside in memory while computations are performed. If the model and data don’t fit in a GPU’s VRAM, that portion of the task can’t be handled by that GPU alone. Today’s advanced GPUs often have large VRAM capacities (e.g. 40 GB or more per GPU), but cutting-edge models still easily push the limits. For instance, just the weights (parameters) of a model like GPT-3 occupy on the order of hundreds of gigabytes when stored at full precision, far more than a single GPU can hold. This is why multiple GPUs are needed – they effectively pool their memory or handle different parts of the model. Techniques like model sharding (splitting the model across GPUs) and memory optimization are used so that each GPU’s VRAM contains a portion of the whole model.

During inference (using a trained model to get results) VRAM is also a key limiter. All the model’s parameters must be loaded into memory for the model to operate. As one source explains, LLMs store their millions to trillions of parameters in VRAM during inference – this accounts for the bulk of memory usage blog.runpod.io. Additionally, when the model processes input text, each layer generates intermediate activation data that temporarily uses VRAM blog.runpod.io. If you try to process multiple inputs at once (a larger batch size for efficiency), you need even more memory to hold those multiple sets of activations concurrently blog.runpod.io. The precision (numeric resolution) of the calculations also affects memory: using lower precision numbers (like FP16 half-precision instead of FP32 full precision) can cut memory usage roughly in half, allowing larger models to fit in the same VRAM blog.runpod.io. Techniques such as quantization (reducing precision) and optimized memory management (like the vLLM library referenced in research) help make better use of limited VRAM so that bigger models or bigger batches can run on a given GPU blog.runpod.io.

From an investor’s perspective, VRAM is a big part of why AI hardware is expensive and in-demand. GPUs with more VRAM are coveted because they can handle larger models and more data at once. When evaluating AI infrastructure, one might consider not just the number of GPUs, but the memory per GPU – it directly impacts whether a certain model can be deployed on that hardware or how many GPUs must work together to serve a model. In summary, VRAM is the high-speed working memory that enables GPUs to chew through vast amounts of data, and sufficient VRAM is essential for running LLMs efficiently blog.runpod.io.

Tensor Operations and Matrix Math

The core computations in deep learning are often described as tensor operations. A tensor is simply a multi-dimensional array of numbers – you can think of it as a generalization of matrices (which are 2D grids of numbers) to potentially higher dimensions. In neural networks, both the data (inputs, outputs) and the model’s parameters (weights) are represented as tensors. Performing a forward pass of a neural network (i.e. computing the output for a given input) involves many linear algebra operations on these tensors. For example, a common operation is matrix multiplication: multiplying an input vector by a weight matrix to produce an output vector. These operations are repeated across many layers and many data samples.

GPUs are extremely efficient at tensor operations because their architecture is built for doing the same arithmetic on many values simultaneously. Imagine you have two large grids of numbers that need to be multiplied together – a GPU can assign a core to handle each part of the grid, effectively doing thousands of small multiplications at the same time and summing the results. Modern GPUs (as mentioned earlier) even have tensor cores dedicated to this task, achieving performance that far exceeds what general-purpose cores can do for matrix math digitalocean.com digitalocean.com. In deep learning, this means that the huge number of calculations required to compute activations and gradients (for training) can be done in parallel at high speed.

It might help to use a quick metaphor: think of a tensor operation like mixing a very large batch of ingredients in a factory. A CPU might be like one chef measuring and mixing each ingredient sequentially for a recipe – fine for a small batch, but very slow for industrial scale. A GPU is like an industrial mixing machine where hundreds of dispensers pour and blend all the needed ingredients at once in a big vat – it achieves the same result much faster by doing everything in parallel. This is why tasks that involve big tensor operations (vision processing, matrix-heavy scientific computing, and of course neural network training) run orders of magnitude faster on GPUs. The term FLOPs (Floating Point Operations per Second) is often used to measure a system’s mathematical throughput. GPU clusters deliver extremely high FLOPs, which is necessary because training an LLM can require quintillions of floating point operations in total. Investors will sometimes hear about “petaFLOPs” or even “exaFLOPs” of compute in the context of AI supercomputers – these numbers reflect the massive parallel math capability provided by many GPUs working together, crunching tensors of data day and night to train models.

Neural Networks and Deep Learning Basics

At the heart of modern AI, including LLMs, are artificial neural networks. These are computational models inspired by the human brain’s networks of neurons. While the details differ, the high-level idea is that we have a collection of simple processing units (neurons or nodes) connected together, and these connections have adjustable strengths (weights). When data is fed into the network (for example, numbers representing words or pixels), it passes through layers of these connected nodes, each layer transforming the data in some way. The network “learns” by adjusting the weights based on examples, a process that occurs during training.

In simpler terms, you can think of a neural network as a series of filters or decision-makers. The first layer takes the raw input and applies some initial processing, the next layer takes that result and refines it further, and so on, until the final layer produces an output (like a prediction or classification). Each connection’s weight determines how strongly one neuron’s output will affect the next neuron. During training, the network compares its output with the desired output (from the training data) and then tunes the weights slightly to reduce errors. This happens over millions of examples – gradually, the network discovers patterns in the data, encoding that knowledge in the weight values. This training process is what we call deep learning when the network has many layers (hence “deep”) enabling it to learn multiple levels of representation.

A key point for investors is that neural networks are the fundamental engine behind AI applications. They are data-driven – rather than being explicitly programmed with rules, they learn from large datasets. The power of deep learning was unlocked in the past decade thanks to three converging factors: (1) big data (we now have enormous datasets to train on, such as text from the internet for LLMs), (2) computing power (GPUs and clusters as discussed, to handle the calculations), and (3) research advancements (improved architectures and algorithms, like the transformer). It’s this combination that allows a neural network with billions of parameters to be trained to perform something as complex as understanding and generating human-like text.

To demystify a bit, let’s use a quick analogy for a neural network: imagine an assembly line for decision-making. Raw information comes in at one end (say, an English sentence). At each station (layer) on the assembly line, a worker performs a simple transformation on the information – perhaps identifying a basic feature. Early layers in a language model might detect simple patterns like word presence or punctuation; later layers aggregate higher-level patterns like grammatical structure or context. By the end of the line, the final layer produces a result – for instance, predicting the next word in the sentence or answering a question. Each worker’s behavior (akin to neuron weights) is not hard-coded but is learned through experience: during training, if the final output was wrong, all the workers adjust their method slightly to try to do better next time. After enough training examples, the assembly line as a whole becomes very good at producing correct outputs. This is essentially what happens in deep learning.

Mathematically, each “worker” (neuron) performs a weighted sum of its inputs and then applies an activation function (a non-linear decision rule), and learning is the process of finding the right weights. But one need not dive into the equations to appreciate that neural networks are flexible function approximators – they can, given enough data and compute, learn extremely complex relationships between inputs and outputs.

Transformers: Powering Modern Large Language Models

The transformer is a specific neural network architecture that has revolutionized natural language processing (NLP) and enabled the current generation of large language models. Introduced in 2017 by the paper “Attention Is All You Need,” the transformer’s key innovation is the self-attention mechanism. In traditional language models (like earlier recurrent neural networks), processing long sentences was challenging because information from earlier words would gradually dilute as the model moved forward. Transformers solved this by allowing every position in the input to attend to (i.e. look at) every other position directly. In practice, this means the model can consider the context of a word based on all the words in the sentence or paragraph, not just the recent ones.

In simple terms, transformer models learn context cloudflare.com. They analyze text and figure out which words or phrases are related or important to each other, even if they are far apart in the sequence. The self-attention mechanism is like a clever spotlight: for each word, the model shines a light on other relevant words in the input, effectively highlighting what to pay attention to when producing the next part of the output. For example, in the sentence “The trophy doesn’t fit in the suitcase because it’s too small.”, a transformer can figure out that “it” refers to the trophy (not the suitcase) by paying attention to the word “small” and understanding that a trophy being small wouldn’t prevent it from fitting – therefore the suitcase must be too small. This kind of contextual reasoning is made possible by self-attention and many layers of processing.

A transformer model is typically organized into multiple layers (often called transformer blocks). Each layer has an attention sublayer and a feed-forward sublayer. The attention sublayer does the work of relating words to each other (it’s often multi-headed, meaning the model looks at different types of relationships in parallel) cloudflare.com. The feed-forward sublayer then processes the attended information (much like a regular neural network layer) to produce an updated representation for each word. Stacking many of these layers (sometimes dozens or more) allows very complex transformations of the input data. The output is a contextualized representation of every word (or token) in the input, which can then be used for various purposes – in an LLM, one common purpose is to predict the next word or to generate a response to a prompt.

One remarkable property of transformers is how well they scale with data and compute. Researchers found that making these models larger (more layers, more neurons, more training data) kept improving performance in tasks like language understanding and generation. This led to the era of large language models – essentially very big transformers trained on massive text datasets. An LLM like GPT-3 or GPT-4 is a transformer with billions (or even trillions) of parameters, trained on hundreds of billions of words. Thanks to transformers, LLMs can capture nuanced patterns of language, from grammar and syntax to semantic meaning and even some world knowledge contained in their training data.

For an intuitive analogy, think of a transformer as a very sophisticated reader. Instead of reading text left-to-right word by word like a conventional approach, our transformer “reader” has the ability to look back and forth at the entire document almost like scanning, finding which pieces of text relate to each other. It’s as if when trying to understand one sentence, the reader can instantly recall any other sentence or word that might inform its meaning. This holistic view enables it to grasp context that a regular sequential reader might miss. That’s why transformers can handle complex tasks like summarizing a document (they can pay attention to relevant points throughout) or translating a paragraph with correct gender/number agreement (by seeing how words relate across the sentence). In essence, the transformer architecture enabled AI to better understand language structure, which is why it powers virtually all state-of-the-art LLMs today cloudflare.com cloudflare.com.

It’s worth noting that while transformers are highly capable, their power comes with the heavy computational requirements we’ve been discussing. All that attention computation is expensive – the cost grows roughly quadratically with the sequence length, meaning very long inputs take a lot of processing. This is another reason we need strong hardware (GPUs) and often many of them in parallel to run large models, especially for long documents or real-time interaction with users. Research is ongoing into more efficient transformer variants or entirely new architectures, but currently the transformer-based LLM is the workhorse of advanced language AI.

Infrastructure Requirements for Running LLMs

Bringing together the concepts above, let’s summarize what an LLM needs to run efficiently and how that translates to infrastructure. Whether you are training a new model from scratch or deploying an existing model to serve customers, the requirements are similar in kind (if not in scale): you need ample compute power, sufficient memory, and fast data handling.

Compute (GPUs and clusters): Large language models demand heavy computation. During training, millions of mathematical operations are performed each second; during inference, generating each token (word piece) involves executing a huge neural network. Therefore, specialized hardware like GPUs (or AI accelerators) is a must. In practice, companies use clusters of GPU-equipped servers to get the necessary compute throughput nebius.com nebius.com. The more GPUs available, the faster a training job can run – though with diminishing returns and greater engineering complexity at very large scales. For inference, scaling out with multiple GPUs or multiple instances allows the system to handle many queries in parallel. From an investment standpoint, this means data centers geared toward LLMs have to provision a significant number of GPU units. We also see cloud providers offering GPU instances by the hour, and businesses must weigh the cost of renting time on existing clusters versus investing in their own hardware.
Memory (VRAM and system memory): As discussed, the model’s size must fit in memory. Training a model might be distributed such that each GPU holds a fraction of the model’s parameters, but collectively the cluster needs to accommodate the full model and the training data batch. For inference, if the model is hosted on a single GPU, that GPU must have enough VRAM for the entire model (which might only be feasible for smaller LLMs or pruned/quantized versions of larger ones). Often, serving a big model requires partitioning it across multiple GPUs or using techniques like model offloading (moving parts of the model in and out of GPU memory from CPU memory as needed, which slows things down). High-end LLM infrastructure will utilize GPUs with large VRAM (40 GB, 80 GB, or more per card, sometimes even using techniques to link GPU memories together) to ensure models can be loaded and run without running out of memory blog.runpod.io. Sufficient system memory and fast storage are also needed to load model weights and feed data to the GPUs quickly.
Storage and Data: Training data for LLMs can be enormous (GPT-3 was trained on roughly 300 billion tokens of text nebius.com, which is hundreds of gigabytes of data). The storage subsystem needs to be able to stream this data to the GPUs without bottlenecking. Often, high-performance SSDs or even networked storage systems are used, possibly coupled with a caching layer to avoid re-reading data repeatedly from a slow disk. In cluster setups, a distributed file system or cloud storage might hold the dataset, and I/O (input/output) throughput becomes a factor. For inference, storage is less of a bottleneck (since the model is loaded into memory and then queries are relatively small), but one still needs reliable storage for model files and any auxiliary data.
Networking: In a multi-GPU cluster, especially a multi-node cluster, the network interconnect is crucial. When GPUs in different servers need to synchronize (for example, exchanging gradient updates during training, or combining results during inference), a slow network can become a major speed limit. This is why specialized high-speed interconnects like InfiniBand or NVIDIA’s NVLink/NVSwitch technology are used in many AI supercomputers en.wikipedia.org. These reduce the communication time between nodes. Think of it as having a faster communication channel in a team: if our puzzle-solving team (from the earlier analogy) constantly has to talk to each other to compare pieces, giving them walkie-talkies (fast network) instead of passing notes (slow network) will significantly speed up the overall job. Investors might hear about network bandwidth when companies boast about their AI infrastructure – it’s a key piece that ensures the expensive GPUs can actually work together efficiently.
Power and Cooling: Though not as glamorous, the practical reality is that GPU-heavy infrastructure consumes a lot of electricity and generates heat. Data centers hosting LLM workloads need robust power delivery and cooling solutions. A single top-tier GPU can draw 300 watts or more; multiply that by hundreds of GPUs in a cluster and one is effectively running the equivalent of a sizable electric installation. Cooling systems (air conditioning, liquid cooling, etc.) are required to keep the hardware from overheating. The need for power and cooling adds to operational costs and is a consideration in the design of AI data centers.

In summary, supporting LLMs is a compute-intensive, memory-hungry, and bandwidth-sensitive endeavor. This drives the trend of specialized AI infrastructure: cloud providers now offer AI-specific compute clusters, and startups are emerging to optimize various pieces (from faster interconnects to better memory management software). For investors, a key takeaway is that not all cloud infrastructure is equal – the AI cloud (for example, GPU instances, TPU pods, etc.) is a distinct segment, and its value comes from enabling the training and deployment of advanced models. Companies that own or efficiently utilize GPU clusters have a strategic advantage in the AI era, similar to how owning steel mills was crucial in the industrial era. Understanding the components and limitations of these systems (e.g., how scaling from 8 to 80 to 800 GPUs isn’t linear due to communication overhead, or how a GPU with twice the VRAM might enable new model capabilities) will help in assessing which players are technologically well-positioned.

Glossary of Key Terms

GPU (Graphics Processing Unit) – A processor with many smaller cores designed for parallel computations. Originally made for rendering graphics, GPUs are now widely used to accelerate AI tasks by performing thousands of operations simultaneously intel.com.
CPU (Central Processing Unit) – The main general-purpose processor in a computer. A CPU has a few cores optimized for sequential task execution and versatility. In AI, CPUs handle orchestration and some data preprocessing, but heavy math is offloaded to GPUs for speed weka.io.
GPU Cluster – A collection of computers (nodes), each with one or more GPUs, networked together to work on large computing tasks in parallel en.wikipedia.org. GPU clusters are used to achieve the massive compute power needed for training and running large AI models that exceed the capability of a single machine nebius.com.
Parallel Processing – The practice of splitting a computational task into independent parts that can be executed at the same time on multiple processors. In the context of GPU clusters, parallel processing is what allows tasks like training an LLM to be completed much faster than serial processing nebius.com.
VRAM (Video Random Access Memory) – High-speed memory on a graphics card dedicated to the GPU’s use blog.runpod.io. VRAM holds the data (inputs, outputs, model weights, etc.) that the GPU cores need for computations. Having sufficient VRAM is critical for deep learning tasks because the model and its intermediate data must fit in memory to be processed blog.runpod.io.
Tensor – In machine learning, a tensor is a multi-dimensional array of numbers (a generalization of vectors and matrices). Tensors are the basic data structures on which neural networks operate. For example, a color image can be represented as a 3D tensor (height × width × color channels of pixels).
Tensor Operations – Mathematical computations on tensors, such as matrix multiplication, addition, etc. Deep learning is essentially a large sequence of tensor operations. Specialized hardware (like GPUs with tensor cores) accelerates these operations and is measured in FLOPs (floating-point operations per second) digitalocean.com.
Neural Network – A computational model inspired by the brain, consisting of layers of interconnected “neurons” (nodes). Each connection has a weight, and learning involves adjusting these weights. Neural networks can learn complex patterns from data and are the foundation of deep learning cloudflare.com.
Deep Learning – A subset of machine learning that uses neural networks with many layers (hence “deep”). Deep learning has been key to recent AI advances, as it can automatically learn representations from large amounts of data, given sufficient compute.
Transformer – A type of neural network architecture particularly suited for sequence data (like text). Transformers use a mechanism called self-attention to consider the relationships between all elements of the input sequence, enabling them to capture long-range dependencies and context very effectively cloudflare.com. Modern large language models are based on transformer architectures.
Large Language Model (LLM) – A massive neural network (often based on a transformer) trained on a very large corpus of text. LLMs can understand and generate human-like text. They are “large” in terms of parameter count (often billions of weights) and training data size en.wikipedia.org cloudflare.com. Examples include OpenAI’s GPT series, Google’s PaLM, Meta’s LLaMA, and others.
Training (of a model) – The process of teaching a neural network by exposing it to a lot of data. In training, the model’s parameters are gradually adjusted (using algorithms like backpropagation) to reduce error on the training examples. Training an LLM is computationally intensive, often requiring parallel processing on GPU clusters for weeks or months nebius.com.
Inference – The process of using a trained model to make predictions or generate outputs from new input data. For LLMs, “doing inference” might mean generating a continuation of a user’s prompt or answering a question. Inference needs to be efficient so that results are returned quickly, which is why GPUs are also used in this phase to accelerate the model’s computations nebius.com.
Parameters (Weights) – The numerical values in a neural network that are learned during training. An LLM with “175 billion parameters” has that many tunable weights. These parameters are what consume memory (VRAM) and are applied in calculations to transform inputs to outputs. More parameters generally allow a model to capture more complex patterns, but also require more compute to train and run.
Throughput – A term describing how much processing can be done per unit time. In an AI context, training throughput might be measured in examples per second processed, and inference throughput could be how many queries per second can be handled. High throughput is achieved by parallelism and efficient use of hardware resources.
Latency – The time delay to get a result. Low latency is especially important in inference (serving user requests). Even if a system has high throughput overall, an individual query should be processed quickly. Strategies like using more GPUs or optimizing model size are sometimes employed to reduce latency for real-time applications.
High-Performance Computing (HPC) – A field of computing focused on aggregating power (through clusters, supercomputers, specialized hardware) to solve large problems quickly. The use of GPU clusters for training AI models is a prime example of an HPC application, overlapping with traditional supercomputing.
Scale/Scalability – Refers to the ability to expand a system’s capacity. In AI infrastructure, scaling might mean adding more GPUs to train a model faster or to serve more users. A well-designed AI system scales efficiently (e.g., doubling the GPUs nearly doubles the training speed, up to certain limits). Investors often consider how scalable a company’s AI solution is, as it relates to how it can handle growth or larger models.

By understanding these concepts and how they interrelate, investors can better grasp why companies make certain technical choices and investments for AI development. Large language models owe their capabilities to the sophisticated dance between advanced algorithms (like transformers) and powerful hardware (like GPU clusters). The synergy of software and hardware is what made the recent AI breakthroughs possible. As the AI cloud sector continues to grow, those equipped with a solid grasp of GPU-based infrastructure will be well-positioned to evaluate which organizations are truly capable of innovating and scaling in the era of large AI models.