.RAPIDS AI, the open-source software to accelerate data science, has just been recognized as a Top #OpenSource Software by InfoWorld. Read about it here: nvda.ws/479CHK4 #RAPIDSAI

We at NVIDIA RAPIDS AI have something exciting for the AI and Data science community. Stay tuned for the announcement on Nov 8th!

RAPIDS brings BIG updates: v24.02 leans into CPU+GPU in RAFT's vector search, RMM host memory pinning, & zero code change NetworkX acceleration; XGBoost 2.0+cuML now available; & deploy it all on Databricks+Dask. Also, RAPIDS GTC24 sessions are scheduled!👉nvda.ws/49HOFf7

We're very happy to announce that NVIDIA and Polars will work together to bring GPU acceleration to Polars DataFrames! 🚀💯 pola.rs/posts/polars-o…

😍48 H100 80GB GPUs from NVIDIA are being set up here at the Chan Zuckerberg Biohub Network . So excited to start using them!

Awesome to see RAPIDS AI 24.04 officially launches cuVS (the library to accelerate vector search on GPU), in addition to pandas 2.0 support, and significant expansions of NetworkX GPU acceleration! medium.com/rapids-ai/rapi…

I'm presenting at PyCon US! Come learn how Python data science workflows using pandas can be accelerated with RAPIDS AI cuDF on NVIDIA GPUs, with just one line of code. #PyCon #DataScience

Announced at #GoogleIO, RAPIDS cuDF is now integrated into Google Colab. RAPIDS cuDF instantly speeds up pandas code by up to 50x with zero code changes in Colab notebooks. Read the tech blog: nvda.ws/3JZPVzo

NVIDIA's Rapids cuDF library is now available by default on Colab. It can accelerate pandas code by up to 50x on Colab with zero code changes. ⚡🐼 Try acceleration on pandas workloads with this 10-minute guide: colab.research.google.com/drive/12tCzP94…

Jensen Huang, Founder and CEO of NVIDIA joined Databricks Co-founder and CEO Ali Ghodsi for a fireside chat on innovation in the age of AI. "Whatever you do, just start," said Huang. "This is a fast-moving train. You don’t want to wait and observe an exponential trend."

From 400x faster simulations to 140x energy savings, see how NVIDIA’s CUDA GPU-accelerated computing is paving the way for sustainable computing. Read more: nvda.ws/3WTGqYI #CUDA #AI #SustainableComputing

🔢 ⚡ To speed up data processing, especially for company data organized in tables, we developed RAPIDS cuDF. Hear our CEO, Jensen Huang, explain how NVIDIA approaches solving complex data processing challenges -- one of the world's most vital workloads.

Together with NVIDIA RAPIDS we bring GPU acceleration to Polars! Read more in our release blog: pola.rs/posts/gpu-engi…

🙌Excited to bring GPU acceleration to polars data - process 100s of millions of rows in seconds on NVIDIA GPUs. ➡️pola.rs/posts/gpu-engi…⚡

We've increased the size of our NVIDIA A100 fleet for paid users by around 2x, and for the last several days we've seen 100% success rate for users requesting A100s.

Say goodbye to slow data processing and GPU memory limits! With Unified Virtual Memory (UVM), RAPIDS cuDF-pandas accelerates pandas operations while handling datasets larger than GPU memory. No code changes needed—just faster results and seamless CPU/GPU integration. RAPIDS AI

Introducing DeepSeek-R1 optimizations for Blackwell, delivering 25x more revenue at 20x lower cost per token, compared with NVIDIA H100 just four weeks ago. Fueled by TensorRT DeepSeek optimizations for our Blackwell architecture, including FP4 performance with state-of-the-art

👀 The latest SGLang inference optimizations on DeepSeek-R1 represent a 26x increase in H100 performance in only 4 months. 📈 🎉 Congrats to the LMSYS Org team.

🛣️ New NVIDIA DRIVE Labs video on the future of mapless driving! High-definition (HD) maps have been essential for autonomous driving, but their cost and maintenance challenges limit deployment. 🚗 In this episode of #DRIVELabs, we explore how NVIDIA is reducing reliance on HD

🙌 7 Python drop-in replacements for massive GPU speedups—no code rewrites required: pandas → %load_ext cudf.pandas polars → .collect(engine="gpu") scikit-learn → %load_ext cuml.accel xgboost → device="cuda" umap → %load_ext cuml.accel hdbscan → %load_ext cuml.accel