👀 LatentView Analytics supercharged their predictive maintenance with RAPIDS AI, achieving up to 639x speedups in feature engineering and 39.8x faster group-by operations⚡ ➡️ nvda.ws/4gy43PI Reduce downtime, lower costs, and optimize performance with accelerated computing.

🚚 #NVIDIAInception startup clicOH revolutionizes the most expensive part of supply chain logistics - last-mile delivery. ClicOH achieved a 20x speedup in route planning and a 15% cost reduction, tackling complex vehicle routing for expanding delivery networks. Read how in

This has been such a great collaboration with polars data ! It's a deep integration that makes GPU acceleration incredibly easy. Bring your queries from minutes down to seconds with virtually no code change.

📣 Announced today, polars data GPU engine powered by RAPIDS AI cuDF is now available in open beta. ➡️ nvda.ws/3zcr7Tj ⚡ 🔢 Accelerate Polars workflows up to 13x on NVIDIA GPUs.

GPU Accelerated Polars — Intuitively and Exhaustively Explained towardsdatascience.com/gpu-accelerate…

📣 Ask CUDA Python questions to experts! Join the LIVE event "CUDA Virtual Connect with Experts" this Friday, September 27th at 10am PT. Discuss CuPy, Numba Numba, RAPIDS @rapidsai, or CUDA Python ecosystem with NVIDIA experts. linkedin.com/posts/katrinar…

📣polars data GPU engine powered by RAPIDSai cuDF is now available in open beta. ⚡Accelerate Polars workflows up to 13x on NVIDIA GPUs. bit.ly/4dsrUgF

📣 Released into open beta: Polars GPU engine powered by RAPIDS cuDF. It brings up to 13x acceleration to the fastest growing dataframe library with zero code changes required. Read the announcement blog from NVIDIA #AIsummit ➡️ nvda.ws/4dzeKP3

The UMAP implementation in cuML in RAPIDs recently got a lot faster: developer.nvidia.com/blog/even-fast…

Today, PyData NYC 🗽 kicked off with a tutorial on GPU Accelerated Python! Numba, CuPy, and RAPIDS AI are all great ways to accelerate your code. Check out the full tutorial on the NVIDIA Accelerated Computing Hub to learn more: github.com/NVIDIA/acceler…

👀 See how RAPIDS cuML 24.10 makes GPU-accelerated #UMAP even faster and scalable to larger-than-GPU-memory datasets, reducing runtime from hours to minutes on 150GB+ datasets. ➡️ nvda.ws/3NYP2cs Dive into the technical walkthrough to learn more.

🚀 The Colab team collaborated closely with NVIDIA to deliver day 1 compatibility for NVIDIA cuML's Zero Code Change ML Acceleration. Now, you can experience significant speedups in your machine learning workflows in Colab with no code modifications! Example notebook below 👇

🎊 Llama Nemotron Ultra 253B is here 🎊 ✅ 4x higher inference throughput over DeepSeek R1 671B 🏆Highest accuracy on reasoning benchmarks: 💎 GPQA-Diamond for advanced scientific reasoning 💎 AIME 2024/25 for complex math 💎 LiveCodeBench for code generation and completion

🎉 Huge congrats to our NVIDIA team “NemoSkills” for winning the AIMO-2 Competition 🏆 on @Kaggle. Their system solved 34 out of 50 problems in just 5 hours using 4 L4 GPUs. 🔢✨⏱️ kaggle.com/competitions/a… How? A powerhouse squad—Christof Henkel, Darragh Hanley, Ivan Sorokin,

🔢 Data > ? Memory 🐏 👀 3 solutions 📹 youtu.be/nyXbTVTKVDM

⚡cuML accelerations for ML workflows for scikit-learn, UMAP and HDBSCAN -- with zero code changes required. cuML is up to 60x faster for UMAP and up to 175x for HDBSCAN over traditional CPU-based implementations. Read the intro to GPU-accelerated UMAP and HDBSCAN workflows:

🔥 Join us at #SciPy2025 for our tutorial, "Scaling Clustering for Big Data: Leveraging RAPIDS cuML" with Allison Ding! 🚀 Come learn to implement, optimize, and scale clustering algorithms effectively unlocking faster, deeper insights in your machine learning workflows.

HUGE > we have a new #1 on the > MTEB Embedding Benchmark Leaderbord llama-embed-nemotron-8b > beats Gemini, Qwen3, Linq, GTE > fits on ONE RTX 5090 > 4096 dims, 32k ctx > 69.46 avg across tasks > multilingual bi-encoder, zero-shot across retrieval, rerank, STS, classification

🎊 Congrats to our NVIDIA cuOpt team who just won the 2025 COIN-OR Cup 🏆, awarded for top #opensource achievements in decision optimization and operations research. cuOpt runs LP, MIP, and VRP workloads on NVIDIA GPUs, delivering fast, scalable results with a robust open

NVIDIA #CUDA-X libraries are accelerating scientific breakthroughs with the power of #AI. 🧬 NVIDIA cuEquivariance takes protein structure analysis from months to minutes, and RAPIDS-singlecell lets researchers process massive volumes of genomics data in seconds. 🔋 NVIDIA