Why GPUs are crucial for machine learning

Introduction

The rise of machine learning, reinforced by the growth of generative AI, has profoundly changed companies' compute needs. Long built on CPU architectures, training now rests largely on GPUs, which have become unavoidable as models reach several billion parameters. In a context where AI workloads are intensifying in French-speaking Switzerland, the ability to run massively parallel computation quickly is asserting itself as a decisive performance factor.

Manufacturers and cloud operators point out that this shift comes above all from the architecture of GPUs themselves. Their design, built to execute a very large number of similar operations simultaneously, matches perfectly the matrix computation at the heart of deep learning. That fit explains their growing adoption, for training as much as for inference.

Massively parallel GPU architecture: the engine of machine learning

Originally designed for graphics rendering, GPUs rest on thousands of cores able to process identical operations in parallel. That contrasts sharply with CPUs, which are optimised for sequential processing. The difference lets GPUs considerably accelerate repetitive matrix operations such as multiplications and convolutions, which are essential to modern machine learning. One accelerator can therefore handle a very large number of neurons or vectors in parallel, which explains the gains observed during intensive training.

Deep learning: models that keep growing and demanding more

The evolution of architectures, whether CNNs, Transformers or LLMs, comes with continuous growth in model size and in the volume of data required.

Frameworks such as PyTorch, TensorFlow and JAX build on the CUDA and ROCm libraries to exploit GPU capabilities fully. Manufacturers indicate that, depending on the configuration, the speed-ups obtained can range from a factor of ten to a hundred compared with CPU execution.

The latest generation of GPUs also includes specialised units such as Tensor Cores, designed to optimise mixed-precision computation. They reduce training time while preserving a comparable level of quality.

Distributed training: going beyond the limits of a single GPU

Modern workloads often exceed the capacity of a single GPU. Cloud infrastructure now relies on clusters fitted with several accelerators, connected by high-throughput interconnects such as NVLink, NVSwitch, PCIe Gen4/Gen5 or RDMA-capable InfiniBand networks.

The distribution frameworks, DeepSpeed, Horovod, Megatron-LM, set out several complementary approaches:

• Data parallelism, duplicating the model and synchronising the gradients,

• Model parallelism, splitting the model into several segments,

• Pipeline parallelism, orchestrating the layers in sequential logic.

These methods allow very large models to be trained within timeframes compatible with organisations' R&D cycles, including in French-speaking Switzerland where AI needs are intensifying.

GPUs and inference, cutting latency and consumption

GPUs also hold a central place in inference environments. They cut latency, particularly in applications needing an immediate response: video processing, continuous analysis, text generation. That improvement rests partly on optimisations such as quantisation, the use of TensorRT graphs, and batching, which strengthen energy efficiency while preserving performance.

Manufacturers mention, in some cases, energy gains of up to a factor of five compared with a CPU architecture.

CPU against GPU: complementarity rather than opposition

CPUs keep an essential role in orchestrating AI pipelines, managing input and output, and running tasks that parallelise poorly. On intensive matrix loads, however, they show their limits.

GPUs, with a greater number of cores, higher memory bandwidth and specialised units, naturally complement CPUs in the heaviest computation phases.

GPUs are therefore not there to replace CPUs, but to complement them in the most demanding phases of machine learning.

What it means for companies

Integrating GPUs profoundly changes how organisations develop and deploy their AI models. Training cycles shorten, research phases accelerate, and large models become more accessible. Companies active in finance, healthcare, industry and cybersecurity notably observe:

• an improvement in application performance;

• a significant reduction in time to market.

In French-speaking Switzerland, several players have already restructured their AI pipelines around GPU infrastructure to support increasingly ambitious projects.

In summary, why GPUs are essential to machine learning

The gains GPUs bring to machine learning rest on several now well-established factors.

Their massively parallel architecture accelerates matrix computation, the heart of modern neural networks, cutting training times significantly. They also offer better efficiency at inference, particularly for real-time applications where latency is a key criterion.

Finally, GPUs make large models easier to handle and fit naturally into distributed architectures, an essential point for companies running multi-GPU clusters or dedicated cloud infrastructure.

Conclusion: an accelerator that has become indispensable

As models grow more complex and data volumes rise, GPUs are establishing themselves as the pillar of modern machine learning. Their parallel architecture, their specialised units and their tight integration with AI frameworks explain their central role.

The developments to come, heterogeneous accelerators, faster interconnects, multi-GPU architectures, should further improve the ability of companies, including in French-speaking Switzerland, to exploit artificial intelligence at scale.

Organisations adopting GPU-capable infrastructure, in house or through sovereign cloud platforms, now hold a decisive advantage in supporting the growth of their AI workloads.