NVIDIA is building more than faster GPUs. We’re building the software and algorithms that help redefine what accelerated computing can do. CUDA-X provides the specialized software acceleration industries need to build what’s next.
Watch how CUDA-X libraries turn GPU compute into real-world applications across engineering, physics and AI, from supply-chain optimization with cuOpt to computational lithography with cuLitho.
What if Studio Ghibli directed Lord of the Rings?
I spent $250 in Kling credits and 9 hours re-editing the Fellowship trailer to bring that vision to life—and I’ll show you exactly how I did it 👇🏼
⚡️Accelerate pandas with up to 28x faster performance and zero code changes.
Watch this walkthrough on accelerating exploratory data analysis and visualization with pandas on NVIDIA GPUs.
Watch now ➡️ https://t.co/dHEbXWB6kK
Looking for an affordable platform to run popular #genAI models at the edge?
Introducing the NVIDIA Jetson Orin Nano Super Developer Kit, offering up to 67 TOPS of AI performance for $249. Existing users can upgrade their JetPack SDK for a "super" boost. https://t.co/BiTCBke58P
Hear Bradley Dice, Senior Software Engineer in GPU-Accelerated Data Analytics at NVIDIA, discuss the effort to hack 'import pandas' to boost its performance using cuDF, a GPU DataFrame library. Sign up for @pycon's event #PyConUS to hear this talk live: https://t.co/UbxcUZs0yp.
NVIDIA just made Pandas 150x faster with zero code changes.
It is now directly integrated in Google Colab.
All you have to do is:
%load_ext cudf.pandas
import pandas as pd
Their RAPIDS library will automatically know if you're running on GPU or CPU and speed up your processing.
You can try it here: https://t.co/oq7qkVWztY
@axccl@top500supercomp This article takes that in account and even with generous numbers, the FLOP/watt seems to be lower than expected: https://t.co/hHZSHYZcPD