The Real Challenge Limiting AI Models Today

NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

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 .

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

The article discusses several recent developments in AI and computing, including the lack of legal rights for machines, the use of SPADE to automate environment generation, and the creation of improved GPU kernels with Hawkeye. It also touches on the differential acceleration of cyber, math, and AI technologies. These topics illustrate ongoing progress in AI infrastructure and performance optimization.

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?

Introducing ShipAI

Towards Data Science has released a video showcase titled "Introducing ShipAI," which highlights real‑world AI work. The post announces this new visual resource and its focus on practical AI applications. It is positioned as a first look into the platform’s capabilities.

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].

Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training

A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration

arXiv:2608.28652v1 Announce Type: new Abstract: Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by sig...

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.