# CUDA-X

Published articles for CUDA-X.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo

DevFeed: [Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo](<https://devfeed.tech/articles/restore-llm-inference-capacity-in-seconds-with-shadow-engine-recovery-in-nvidia-dynamo-6928.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/restore-llm-inference-capacity-in-seconds-with-shadow-engine-recovery-in-nvidia-dynamo/>)

Author: Michelle Horton

Published: 2026-08-25T20:57:54Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [sglang](<https://devfeed.tech/topics/sglang.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [process](<https://devfeed.tech/tags/process.md>)

### AI overview

This article explains NVIDIA Dynamo's shadow engine recovery for LLM inference. A fully initialized shadow engine shares weights with the active engine and takes over after a process failure, reducing recovery from a 283-second cold restart to 7.3 seconds in the described two-worker deployment.

### Source excerpt

When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...

## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy

DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy](<https://devfeed.tech/articles/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy-6933.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-massive-scale-umap-in-minutes-using-multiple-gpus-without-losing-accuracy/>)

Author: Tanya Lenz

Published: 2026-08-18T16:48:08Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [RAPIDS](<https://devfeed.tech/topics/rapids.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [feature](<https://devfeed.tech/tags/feature.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [post](<https://devfeed.tech/tags/post.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training](<https://devfeed.tech/tags/training.md>), [vector](<https://devfeed.tech/tags/vector.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy.

### Source excerpt

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...

## Run High-Performance Core Math at Scale with NVIDIA nvmath-python

DevFeed: [Run High-Performance Core Math at Scale with NVIDIA nvmath-python](<https://devfeed.tech/articles/run-high-performance-core-math-at-scale-with-nvidia-nvmath-python-6931.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-high-performance-core-math-at-scale-with-nvidia-nvmath-python/>)

Author: Michelle Horton

Published: 2026-07-30T22:43:04Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [math](<https://devfeed.tech/topics/math.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NumPy](<https://devfeed.tech/topics/numpy.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [pip](<https://devfeed.tech/topics/pip.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [device-apis](<https://devfeed.tech/tags/device-apis.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [scale](<https://devfeed.tech/tags/scale.md>)

### AI overview

NVIDIA nvmath-python 1.0 provides a Pythonic interface to CUDA-X and NVPL math libraries, enabling optimized numerical operations on CPUs, CUDA GPUs, and distributed multi-GPU, multi-node systems. The article covers its sparse-tensor approach, flexible installation options, and interoperability with NumPy, CuPy, and PyTorch.

### Source excerpt

NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users...

## Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing

DevFeed: [Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing](<https://devfeed.tech/articles/advancing-semiconductor-innovation-across-materials-engineering-and-manufacturing-6759.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/advancing-semiconductor-innovation-across-materials-engineering-and-manufacturing/>)

Author: Tanya Lenz

Published: 2026-07-27T00:45:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [computational-chemistry-materials-science](<https://devfeed.tech/tags/computational-chemistry-materials-science.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [industrial-digitalization-digital-twin](<https://devfeed.tech/tags/industrial-digitalization-digital-twin.md>), [manufacturing](<https://devfeed.tech/tags/manufacturing.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [physics](<https://devfeed.tech/tags/physics.md>), [production](<https://devfeed.tech/tags/production.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>)

### AI overview

Applied Materials and NVIDIA are presented as combining materials engineering, semiconductor manufacturing, CUDA-X libraries, GPU-accelerated simulation, physics-based modeling, and AI-driven digital twins in an end-to-end digital development model. The approach spans atomic-scale materials discovery, process development, and factory optimization, with Ginestra used to connect material defects and properties to predicted device performance.

### Source excerpt

As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have...

## Designing GPU-Accelerated Query Engines with NVIDIA GQE

DevFeed: [Designing GPU-Accelerated Query Engines with NVIDIA GQE](<https://devfeed.tech/articles/designing-gpu-accelerated-query-engines-with-nvidia-gqe-6799.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/designing-gpu-accelerated-query-engines-with-nvidia-gqe/>)

Author: Michelle Horton

Published: 2026-06-30T17:36:43Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [IO](<https://devfeed.tech/topics/io.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Parser](<https://devfeed.tech/topics/parser.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [compression](<https://devfeed.tech/tags/compression.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data](<https://devfeed.tech/tags/data.md>), [data-analytics-processing](<https://devfeed.tech/tags/data-analytics-processing.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [databases](<https://devfeed.tech/tags/databases.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [performance](<https://devfeed.tech/tags/performance.md>), [sql](<https://devfeed.tech/tags/sql.md>)

### AI overview

This article presents GQE, a reference architecture for executing SQL queries on GPUs. It explains how NVIDIA hardware and CUDA-X libraries address memory, I/O, data movement, decompression, and end-to-end performance challenges for large datasets.

### Source excerpt

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances--including high bandwidth memory (HBM), NVIDIA...