The Real Challenge Limiting AI Models Today

The Hardware That Makes AI Possible

CPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science .

When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI

Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .

How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation

Exploring GPU acceleration with cuDF, cudf. pandas, and the Polars GPU Engine The post How Much of a Data Science Workflow Can Run on a GPU Today?

AI and compute

We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3. 4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)[^footnote-correction].

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

arXiv:2608. 12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance.

Announcing the OpenAI Learning Accelerator

Accelerating engineering cycles 20% with OpenAI

Accelerating engineering cycles 20% with OpenAI.

BluTrain: A C++/CUDA Framework for AI Systems

arXiv:2606. 24780v1 Announce Type: new Abstract: Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less by the architecture itself than by how that architecture is expressed on the hardware.

A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design

arXiv:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.

A Day in the Life of a Data Scientist in 2026

How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .

Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness

arXiv:2409. 07609v3 Announce Type: replace-cross Abstract: Deploying adversarially robust machine learning systems requires continuous trade-offs between robustness, cost, and latency.

Extensions and limitations of the neural GPU