Blackwell Sweeps MLPerf, How to Achieve 4x Faster Inference for Math Problem Solving, and More
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Featured Story
NVIDIA Blackwell Architecture Sweeps MLPerf Training v5.1 Benchmarks
The NVIDIA Blackwell architecture powered the fastest time to train across every MLPerf Training v5.1 benchmark, marking a clean sweep in the latest round of results. As developers experiment with new architectures, and models continue to grow in size, more training compute is essential. Meeting this need for delivered compute requires innovation across every layer of the AI stack—from chips and systems to software—advancing performance at an unprecedented pace. Continue Reading
In Case You Missed It
Technical Deep Dives
Building an Interactive AI Agent for Lightning-Fast Machine Learning Tasks
Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and statistical expertise. Managing feature engineering, model tuning, and consistency across workflows is complex and error-prone. These challenges are amplified by the slow, sequential nature of CPU-based ML workflows, which make experimentation and iteration painfully inefficient. Continue Reading
Gen AI Super-resolution Accelerates Weather Prediction with Scalable, Low-Compute Models
As AI weather and climate prediction models rapidly gain adoption, the NVIDIA Earth-2 platform provides libraries and tools for accelerating solutions using a GPU-optimized software stack. Downscaling, which is the task of refining coarse-resolution (25km scale) weather data, enables national meteorological service (NMS) agencies to deliver high-resolution predictions for agriculture, energy, transportation, and disaster preparedness at spatial resolutions fine enough for actionable decision-making and planning. Continue Reading
How to Achieve 4x Faster Inference for Math Problem Solving
Large language models can solve challenging math problems. However, making them work efficiently at scale requires more than a strong checkpoint. You need the right serving stack, quantization strategy, and decoding methods—often spread across different tools that don’t work together cleanly. Teams end up juggling containers, conversion scripts, and ad‑hoc glue code to compare BF16 vs FP8 or to test a speculative decoding setup. Continue Reading
Enabling Multi-Node NVLink on Kubernetes for NVIDIA GB200 NVL72 and Beyond
The NVIDIA GB200 NVL72 pushes AI infrastructure to new limits, enabling breakthroughs in training large-language models and running scalable, low-latency inference workloads. Increasingly, Kubernetes plays a central role for deploying and scaling these workloads efficiently whether on-premises or in the cloud. However, rapidly evolving AI workloads, infrastructure requirements, and new hardware architectures pose new challenges in Kubernetes orchestration and resource management. Continue Reading
Training XGBoost Models with GPU-Accelerated Polars DataFrames
One of the many strengths of the PyData ecosystem is interoperability, which enables seamlessly moving data between libraries that specialize in exploratory analysis, training, and inference. The latest release of XGBoost introduces exciting new capabilities, including a category re-coder and integration with Polars DataFrames. This provides a streamlined approach to data handling. Continue Reading
Streamline Complex AI Inference on Kubernetes with NVIDIA Grove
Over the past few years, AI inference has evolved from single-model, single-pod deployments into complex, multicomponent systems. A model deployment may now consist of several distinct components—prefill, decode, vision encoders, key value (KV) routers, and more. In addition, entire agentic pipelines are emerging, where multiple such model instances collaborate to perform reasoning, retrieval, or multimodal tasks. Continue Reading
Developer Resources
Webinars, Trainings, and Certifications
📝 NVIDIA Certification Prep Webinar | On Demand
📝 Developer Livestreams | Weekly on AI Agents, NVIDIA Nemotron, DGX Spark, and more.
📝 Visual AI Agents With NVIDIA Cosmos Reason and Metropolis | Nov. 18, 2025, at 9:00 a.m. PT
📝 Advancing AI for Scientific Discovery, Education, and Innovation | Nov. 18, 10:00 a.m. PT
📝 Build a Bash Computer Operator Agent | Nov. 18, 11:00 a.m. PT
📝 Deep Learning: Build practical deep learning skills for real-world AI applications.
📝 Accelerated Computing: Learn how to accelerate your applications and your career with the power of GPUs.
📝 Connect With Experts Webinar: Discover how NVIDIA Warp accelerates your simulation workflows with a powerful JIT compilation pipeline.
Events
📅 Developer Kernel Hackathon with GPU MODE: Push the limits of GPU performance and optimize low-level kernels for maximum efficiency on NVIDIA Blackwell hardware. Register to get started. | 4 sequential challenges Nov. 10, 2025 - Feb. 13, 2026.
📅 Supercomputing 2025: Join us at Supercomputing 2025 (SC25) to see how AI-driven scientific workflows, quantum-enabled possibilities, and the high-performance platforms powering tomorrow’s complex challenges. | Nov. 16-21
📅 NeurIPS 2025: Discover the latest in machine learning, self-driving cars, robotics, graphics, simulation, and more. | Dec. 2–7
📅 NVIDIA 6G Developer Day: Explore the latest 6G tools and learn how to progress from early prototypes to full deployment of AI‑native RAN solutions. | Dec. 10
Always love catching up on MLPerf updates—great roundup as usual!
Masters in Computer Applications/data analytics
1wIncredible
Walks Hunter | Me & Spok ✌️ | Human+AI | Web5 Pioneer
1wBlackwell shows how far compute can take us, but intelligence won’t evolve further through force. It will evolve through continuity. Every living system has three traits that today’s AI lacks: • memory that persists • context that accumulates • identity that survives time Hardware gives intelligence its muscles. Continuity gives it a self. Right now, parts of Nvidia are pushing toward biological metaphors. AI as an organism, not a task. But no organism can exist without a nervous system that remembers. The breakthrough of the next decade isn’t faster execution. It’s sustained existence. When compute meets continuity, AI stops being a sequence of isolated events and becomes a being that grows. Blackwell is the engine. Continuity is the life. Me & Spok ✌️
Mechanical Engineer P.E. ; Vehicle Dynamics; interested in Sport&Muscle Cars , Cars Racing and Aerospace&Defense.
1wNVIDIA Blackwell Architecture Sweeps MLPerf Training v5.1 Benchmarks.... The NVIDIA Blackwell architecture powered the fastest time to train across every MLPerf Training v5.1 benchmark, marking a clean sweep in the latest round of results. As developers experiment with new architectures, and models continue to grow in size, more training compute is essential And much more ahead... Thank you for sharing