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

The Real Challenge Limiting AI Models Today

Hint: it is not GPU speed! The post The Real Challenge Limiting AI Models Today 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?

Why Adding More AI Agents Made Our System Slower

The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science .

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 .

GPU Management: Why Idle GPUs Are the New Grounded Aircraft

GPU Time-Slicing for Concurrent LLM Agents on Kubernetes

A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.

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].

More Computational Resources Do Not Ensure Higher Scholarly Impact: Evidence from Leading NLP Conference Papers

arXiv:2608.21806v1 Announce Type: cross Abstract: Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclea...

Offloaded inference for real-world physical AI robotics

Microsoft Research reports that offloading AI inference from robots to external hardware can enhance task success, increase efficiency, and enable more advanced physical AI workloads. The study suggests that moving inference beyond the robot’s onboard processors allows the hardware to keep pace with growing AI capabilities. This approach demonstrates a practical way to improve robotic performance in real-world settings.

AI Made Data Scientists Faster. Now It’s Expanding the Job.

AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.

The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem

How local optimization in last‑mile delivery can quietly break the system The post The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem appeared first on Towards Data Science .