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?
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.
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 .
Hint: it is not GPU speed! The post The Real Challenge Limiting AI Models Today appeared first on Towards Data Science .
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.
The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies.
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination.
arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.