Why GPUs Are the Engine Behind Large Language Models

Large language models (LLMs) like GPT-4, Meta's Llama, and Google's Gemini have moved from research curiosity to mainstream tools used in everything from customer support to software development. Behind every response these models generate sits a substantial amount of hardware doing very heavy computational work. Understanding which hardware matters, and why, is increasingly relevant for anyone building, deploying, or investing in AI systems.

How GPUs Differ From Traditional Processors

A central processing unit (CPU) is the standard chip found in most computers. CPUs are designed to handle a relatively small number of complex tasks in sequence, which makes them excellent for general-purpose computing. A graphics processing unit (GPU), by contrast, is built around thousands of smaller cores that can execute many operations at the same time.

According to NVIDIA's own technical documentation, this parallel architecture makes GPUs highly efficient for the kinds of tasks that dominate AI workloads, particularly matrix multiplications, which are the mathematical foundation of neural networks. Where a CPU might process operations one after another, a GPU processes thousands simultaneously. For LLMs, which involve billions of parameters and enormous datasets, this difference in architecture is not a minor detail. It is the reason GPU-based systems can complete training runs in days rather than years.

What Happens During LLM Training

Training an LLM means feeding the model enormous quantities of text and repeatedly adjusting its internal parameters until it learns to predict language patterns accurately. This process involves two core mathematical operations: forward passes, where data moves through the network to generate a prediction, and backpropagation, where errors are calculated and the model's weights are updated accordingly.

Both operations require dense matrix arithmetic performed across billions of parameters. According to the Artificial Intelligence School, GPUs accelerate this process by distributing the computational load across their many cores, handling the parallel nature of these calculations far more efficiently than CPUs can. A single training run for a large model can involve trillions of individual calculations, and the speed at which a GPU can execute floating-point operations (arithmetic involving decimal numbers) directly determines how long that training run takes.

What Happens During Inference

Inference is the phase where a trained model is actually used. When you type a question into an AI assistant and receive a response, the model is running inference. It processes your input, passes it through the trained network, and generates output token by token (a token is roughly equivalent to a word or part of a word).

As noted by the Blockchain Council, GPUs reduce latency during inference by managing multiple computations concurrently. This matters enormously at scale. A service handling thousands of simultaneous user queries needs hardware that can process many requests in parallel without queuing them up and making users wait. GPUs are well suited to this because their architecture mirrors the parallel nature of serving multiple requests at once.

GPUs Built for LLM Workloads

Not all GPUs are equal when it comes to LLM tasks. Consumer-grade gaming GPUs can run smaller models, but serious training and large-scale inference require data centre class hardware. NVIDIA dominates this segment with two flagship products that have become industry standards.

The NVIDIA A100, released in 2020, introduced Tensor Cores (specialised processing units designed to accelerate the matrix operations common in deep learning) and high-bandwidth memory that allows the GPU to move data quickly between storage and processing units. The A100 became the workhorse of most major LLM training projects in the early 2020s.

The NVIDIA H100, the successor to the A100, pushes performance considerably further. According to data from Birow's GPU performance analysis, the H100 delivers 67 TFLOPS (teraflops, or trillions of floating-point operations per second) of FP32 performance and 1,979 TFLOPS of FP16 performance. FP32 and FP16 refer to different levels of numerical precision, with FP16 being a lower-precision format that is faster to compute and commonly used during training and inference without meaningful loss of model quality.

NVIDIA has since released the Blackwell architecture, represented by the B200 GPU. According to Tom's Hardware, a DGX B200 node using eight Blackwell GPUs achieved over 1,000 tokens per second per user when running Meta's Llama 4 Maverick model. This represented a 31% improvement over the previous performance record for that benchmark. Tokens per second is a practical measure of inference speed and directly affects how responsive an AI application feels to end users.

Reading the Performance Numbers

GPU performance for AI workloads is typically expressed in FLOPS or TOPS (trillion operations per second). Higher numbers indicate greater raw computational capacity. However, raw compute is only part of the picture. Memory bandwidth, the rate at which data can be moved in and out of the GPU's memory, is equally important for LLM tasks because these models require constant access to large parameter sets stored in memory.

According to NVIDIA's blog, GPU performance for AI inference has improved by roughly 1,000 times over the past decade. This trajectory reflects both architectural improvements and the development of specialised features like Tensor Cores that are purpose-built for the matrix operations LLMs depend on.

Alternatives to GPUs

GPUs are not the only option for LLM workloads. Google developed Tensor Processing Units (TPUs) specifically for machine learning tasks, and according to IT Pro, TPUs offer competitive performance and energy efficiency compared to GPUs in certain contexts. Google uses TPUs extensively for its own AI infrastructure, including training and serving its Gemini models.

TPUs are generally less accessible to organisations outside of Google's cloud ecosystem, though Google Cloud does offer TPU instances. For most organisations, GPUs remain the more flexible and widely available option, with a broader ecosystem of software tools and community support.

Cost and Practical Access

High-performance data centre GPUs carry significant price tags. NVIDIA H100 units have been listed at prices ranging from roughly $25,000 to over $40,000 per card depending on configuration and market conditions, though prices fluctuate. For organisations that cannot justify that capital expenditure, cloud providers including AWS, Google Cloud, and Microsoft Azure offer GPU instances billed by the hour or second.

Cloud access lowers the barrier to entry considerably and allows teams to scale compute up or down based on demand. The trade-off is that sustained cloud usage at scale can become expensive over time compared to owning hardware outright. The right choice depends on the scale of the workload, the frequency of use, and the organisation's capital versus operational expenditure preferences.

The Hardware Foundation of Modern AI

GPUs have become the foundational hardware layer for LLMs because their parallel processing architecture aligns precisely with the mathematical demands of training and inference. The NVIDIA H100 and Blackwell-series GPUs currently represent the high end of what is available for these workloads, with measurable performance advantages over previous generations. Alternative hardware like TPUs exists and is competitive in specific contexts, but GPUs remain the dominant choice across the industry.

As LLMs continue to grow in scale and application, the hardware running them will remain a critical variable in what is possible, how fast results arrive, and what it costs to deliver them. For anyone building or evaluating AI systems, understanding the GPU layer is not optional background knowledge. It is central to making informed decisions about architecture, cost, and capability.