# cudnn GPU-accelerated library of primitives for deep neural networks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements DevFeed: [OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:05:23Z Content type: news Language: en Sources: [Phoronix]() Topics: [Data structures](), [Tooling](), [x86](), [releases](), [cudnn](), [Open Source]() Tags: [avx](), [command-line](), [cuda](), [data](), [data-structure](), [desktop-linux](), [github](), [kernels](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [x86]() ### AI overview OpenVDB 13.1 introduces a SIMD framework using Agner Fog's VectorClass Library for explicit x86 vectorization up to AVX-512. Adapted point transfer algorithms reportedly achieve 2x to 4x performance improvements on modern AVX-512 x86_64 CPUs. The release also includes NanoVDB CUDA resource-management and kernel improvements, plus updates to vdb_tool. ### Source excerpt OpenVDB is the sparse volume data structure and tooling library maintained by the Academy Software Foundation. OpenVDB in turn is used by various CGI software for dealing with sparse volumetric data such as Houdini, RenderMan, and Cinema 4D to the open-source Blender. It's even won an Academy Award for technical achievement while now in 2026 it's finally establishing a SIMD framework for better leveraging modern x86 ISA capabilities... ## What's new in TensorFlow 2.18 DevFeed: [What's new in TensorFlow 2.18]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-10-28T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [releases](), [LiteRT](), [NumPy](), [CUDA](), [cudnn](), [NCCL](), [Google]() Tags: [announcement](), [cuda](), [cudnn](), [gpu](), [litert](), [migration](), [nccl](), [nvidia](), [nvidia-rtx](), [python](), [release](), [releases](), [reproducible-builds](), [tensorflow](), [tensorflow-core]() ### AI overview TensorFlow 2.18 introduces NumPy 2.0 compatibility updates, transitions TFLite development to the LiteRT repository, and adds hermetic CUDA, cuDNN, and NCCL dependencies for more reproducible source builds. Binary packages add dedicated kernels for compute capability 8.9 GPUs, including NVIDIA RTX 40 series, L4, and L40, while dropping precompiled support for Maxwell GPUs. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core NumPy 2.0 The upcoming TensorFlow 2.18 release will include support for NumPy 2.0. While the majority of TensorFlow APIs will function seamlessly with NumPy 2.0, this may break some edge cases of usage, e.g., out-of-boundary conversion errors and numpy scalar representation errors. You can consult the following common solutions. Note that NumPy's type promotion rules have been changed (See NEP 50 for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results. Please see the NumPy 2 migration guide. We've updated some TensorFlow tensor APIs to maintain compatibility with NumPy 2.0 while preserving the out-of-boundary conversion behavior in NumPy 1.x. LiteRT Repository We're making some changes to how LiteRT (formerly known as TFLite) is developed. Over the coming months, we'll be gradually transitioning TFLite's codebase to LiteRT. Once the migration is complete, we'll start accepting contributions directly through the LiteRT repository. There will no longer be any binary TFLite releases and developers should switch to LiteRT for the latest updates. Hermetic CUDA If you build TensorFlow from source, Bazel will now download specific versions of CUDA, CUDNN and NCCL distributions, and then use those tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions because the build no longer relies on the locally installed versions. More details are provided here. CUDA Update TensorFlow binary distributions now ship with d ## Build AI-powered applications using OpenLLM and Vultr Cloud GPU DevFeed: [Build AI-powered applications using OpenLLM and Vultr Cloud GPU]() Original publisher: [Read original article]() Author: vultr Published: 2023-12-12T00:00:00Z Content type: tutorial Language: en Sources: [MDN Blog]() Topics: [Artificial Intelligence](), [Large Language Model](), [Cloud](), [GPU](), [API](), [nginx](), [SSL](), [Security](), [CUDA](), [cudnn](), [Python](), [PyTorch]() Tags: [ai](), [api](), [article](), [cloud](), [cuda](), [cudnn](), [gpu](), [guide](), [large-language-model](), [nginx](), [python](), [pytorch](), [security](), [ssl]() ### AI overview This tutorial explains how to deploy the Falcon 7B model with OpenLLM on a Vultr Cloud GPU server and generate API responses for AI-powered applications. It covers server provisioning, dependency installation, persistent OpenLLM service setup, Nginx reverse-proxy configuration, load balancing, and SSL-based HTTPS security. ### Source excerpt Learn how to build AI-powered apps using OpenLLM and Vultr Cloud GPU. This guide shows how to generate API responses using a Large Language Model. It also covers instructions for setting up an Nginx server and implementing SSL security. ## Running Tensorflow on AWS GPUs DevFeed: [Running Tensorflow on AWS GPUs]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2017-02-26T23:41:27Z Content type: tutorial Language: en Sources: [Nelson Elhage]() Topics: [Tensorflow](), [Amazon Web Services](), [Amazon EC2](), [CUDA](), [cudnn](), [Ubuntu]() Tags: [aws](), [cuda](), [cudnn](), [ec2](), [gpu](), [tensorflow](), [ubuntu]() ### AI overview A tutorial-style post describing how to prepare an Ubuntu Amazon EC2 GPU environment for TensorFlow model training. It covers creating an AMI with NVIDIA drivers, CUDA, cuDNN, and GPU-enabled TensorFlow, along with kernel-driver installation issues. ### Source excerpt I've been spending some time learning deep learning and tensorflow recently, and as part of that project I wanted to be able to train models using GPUs on EC2. This post contains some notes on what it took to get that working. As many people have commented, the environment setup is often the hardest part of getting a deep learning setup going, so hopefully this will be useful reference to someone. ## Caffe: Brew your first DNN DevFeed: [Caffe: Brew your first DNN]() Original publisher: [Read original article]() Published: 2015-07-29T11:49:40Z Content type: tutorial Language: en Sources: [Alex Korbonits]() Topics: [Deep learning](), [CUDA](), [Python](), [cudnn](), [Homebrew](), [GPU](), [Google]() Tags: [caffe](), [code](), [cuda](), [cudnn](), [deep-learning](), [dnn](), [gpu](), [guide](), [homebrew](), [how-to](), [installation]() ### AI overview A hands-on guide to installing and configuring Caffe to build a deep neural network, including prerequisites such as CUDA, cuDNN, Homebrew, Python, NumPy, and an NVIDIA GPU for GPU mode. It also introduces Caffe's Model Zoo and its use for application-focused experimentation. ### Source excerpt A hands-on guide to Caffe -- how to brew your first deep neural network and run Google Deep Dream.