# CUDA CUDA is NVIDIA's accelerated-computing platform, providing software that enables applications to use GPUs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design DevFeed: [NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T16:47:58Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [CUDA](), [Quantum Computing](), [Nvidia](), [Orchestration](), [Open Source](), [Algorithm]() Tags: [ai](), [algorithm](), [applications](), [cuda](), [enterprise](), [nvidia](), [open-source](), [orchestration](), [quantum]() ### AI overview NVIDIA added CUDA-Q Logical to its open-source CUDA-Q platform for designing applications on fault-tolerant quantum computers. Early-access reports say Fermilab reduced an algorithm design cycle from five months to three weeks, while Iceberg Quantum modeled a Diraq spin-qubit architecture using about 150,000 physical qubits for 1,000 logical qubits. ### Source excerpt NVIDIA has added CUDA-Q Logical to its open-source CUDA-Q platform, an orchestration layer for building applications that run on fault-tolerant quantum computers, and it arrives with two numbers that are interesting. Fermilab says the tool cut a fault-tolerant algorithm design cycle from five months to three weeks, and Iceberg Quantum used it to show that The post NVIDIA CUDA-Q Logical Debuts With a 7x Fermilab Speedup and a 10x Cut in Diraq's Qubit Estimate appeared first on StorageReview.com. ## CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs DevFeed: [CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs]() Original publisher: [Read original article]() Author: Jonathan Bentz Published: 2026-09-09T20:24:12Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [GPU](), [NVLink](), [Orchestration]() Tags: [cli](), [cuda](), [cuda-tile](), [developer-tools-techniques](), [gpu](), [nsight-tools-compute](), [nvidia](), [nvlink](), [release](), [simulation-modeling-design](), [top-stories](), [windows]() ### AI overview CUDA Toolkit 13.4 adds Windows on Arm support, preview support for the NVIDIA Rubin GPU architecture, and new GPU-sharing controls through MPS V3. It also introduces CUDA Compute Fabric Transport for data movement across NVIDIA NVLink fabric. ### Source excerpt Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software... ## Cut GPU inference cold start from 8 minutes to less than a minute DevFeed: [Cut GPU inference cold start from 8 minutes to less than a minute]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-03T18:30:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [GPU](), [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [CUDA](), [Optimization](), [Amazon S3](), [Kubernetes]() Tags: [ai-infrastructure](), [amazon-eks](), [aws-marketplace](), [cpu](), [cuda](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [optimization](), [post-contributed](), [s3](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview The article measures GPU model startup from pod creation to the first inference response and identifies six sequential phases. It reports that CUDA kernel recompilation dominates startup for a 64 GB model, while S3 weight downloads dominate for a 203 GB model. Configuration and platform changes reduced warm-node startup times by 80-93%, with additional cold-node improvements requiring Amazon EKS Auto Mode. ### Source excerpt We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold start from 8 minutes to less than a minute appeared first on The New Stack. ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## 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]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-25T20:57:54Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Large Language Model](), [Inference](), [model-deployment](), [Nvidia](), [Optimization](), [CUDA](), [Processes](), [GPU](), [NCCL](), [sglang]() Tags: [agentic-ai-generative-ai](), [cuda](), [cuda-x](), [data-science](), [deployment](), [developer-tools-techniques](), [gpu](), [inference](), [llm](), [models](), [nccl](), [nvidia](), [optimization](), [process]() ### 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,... ## CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access DevFeed: [CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-25T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Python](), [GPU](), [C++](), [toolchain](), [PyTorch](), [RAPIDS](), [releases]() Tags: [c-plus-plus](), [cuda](), [data-science](), [developer-tools-techniques](), [gpu](), [numba](), [python](), [pytorch](), [rapids](), [release](), [toolchain]() ### AI overview CUDA Python 1.0 makes the CUDA platform accessible from Python through stable, versioned components, including runtime access, low-level CUDA C API bindings, parallel algorithms, component discovery, and NVIDIA math libraries. The release establishes a shared foundation for Python GPU libraries and introduces semantic-versioning commitments for public APIs. ### Source excerpt For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and... ## Hot Chips 2026: CUDA Targets RISC-V DevFeed: [Hot Chips 2026: CUDA Targets RISC-V]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-24T16:49:25Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [CUDA](), [RISC-V](), [Nvidia](), [GPU](), [Software]() Tags: [cache](), [cuda](), [framework](), [gpu](), [nvidia](), [pcie](), [risc-v](), [software](), [uefi]() ### AI overview The article reports that Nvidia is exploring support for CUDA on RISC-V CPUs. It describes requirements including RVA23, server SoC and platform specifications, RISC-V ACPI support, vector extensions, and PCIe coherency to support reliable and efficient GPU compute. ### Source excerpt CUDA is the most important software framework in the GPU compute world, and Nvidia is looking at supporting CUDA on RISC-V. Terms and conditions may apply. ## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-18T18:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [ALCHEMI](), [AI-assisted coding](), [Agent Skills](), [PyTorch](), [CUDA](), [GPU](), [Python](), [AI Development]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [ai-coding](), [alchemi](), [coding](), [computational-chemistry-materials-science](), [compute](), [cuda](), [developer-tools-techniques](), [featured](), [gpu](), [python](), [pytorch](), [simulation-modeling-design]() ### AI overview This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines. ### Source excerpt Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the... ## NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation DevFeed: [NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-11T19:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [SDKs](), [CUDA](), [GPU](), [AI Inference](), [Inference Performance](), [Latency](), [Low Latency](), [C++](), [Python](), [Artificial Intelligence](), [Automation]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [automation](), [c-plus-plus](), [cuda](), [edge-computing](), [featured](), [gpu](), [inference](), [jetpack](), [jetson](), [latency](), [low-latency](), [nvidia](), [python](), [robotics](), [robotics-compute](), [sdks](), [video-analytics](), [video-codec-sdk]() ### AI overview NVIDIA JetPack 7.2.1 adds PyNvVideoCodec 2.2 support on Jetson Thor, enabling Python-based hardware video encoding and decoding with GPU-resident frames. It also introduces agentic video skills that turn developer goals into device inspection, configuration, execution, measurement, and evidence-driven codec workflows. ### Source excerpt Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media... ## Pulling multi-gigabyte container images in seconds on Amazon EKS DevFeed: [Pulling multi-gigabyte container images in seconds on Amazon EKS]() Original publisher: [Read original article]() Author: Sri Saran Balaji Vellore Rajakumar Published: 2026-08-10T16:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Amazon Elastic Kubernetes Service](), [container images](), [Machine learning](), [autoscaling](), [CUDA]() Tags: [ai-infrastructure](), [amazon-eks](), [autoscaling](), [aws-marketplace](), [container](), [container-images](), [containers](), [cuda](), [kubernetes](), [machine-learning](), [post-contributed](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview This article explains how a production ML platform on Amazon EKS reduced multi-minute pulls of roughly 30 GB container images to seconds. Profiling found that software use of available hardware, rather than network bandwidth or the registry, was the main constraint; the resulting changes are available by default in EKS Auto Mode and were contributed upstream to containerd and the SOCI snapshotter. ### Source excerpt When the image is the bottleneck: Machine learning changed what a container image looks like. A typical application ships in The post Pulling multi-gigabyte container images in seconds on Amazon EKS appeared first on The New Stack. ## Run High-Performance Core Math at Scale with NVIDIA nvmath-python DevFeed: [Run High-Performance Core Math at Scale with NVIDIA nvmath-python]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-30T22:43:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Python](), [CUDA](), [math](), [GPU](), [NumPy](), [cpu](), [PyTorch](), [pip]() Tags: [agentic-ai-generative-ai](), [c-plus-plus](), [ci-cd](), [cpu](), [cuda](), [cuda-x](), [data-science](), [developer-tools-techniques](), [device-apis](), [featured](), [gpu](), [multi-gpu](), [nvidia](), [performance](), [python](), [pytorch](), [scale]() ### 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]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-27T00:45:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [CUDA](), [GPU](), [Optimization](), [Nvidia](), [Simulation and Design](), [Hardware]() Tags: [ai](), [computational-chemistry-materials-science](), [compute](), [cuda](), [cuda-x](), [data-center-cloud](), [engineering](), [featured](), [gpu](), [hardware](), [industrial-digitalization-digital-twin](), [manufacturing](), [nvidia](), [optimization](), [physics](), [production](), [simulation-modeling-design]() ### 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]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-06-30T17:36:43Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Databases](), [NCCL](), [CUDA](), [NVLink](), [Compression](), [IO](), [SQL](), [Parser](), [Streaming](), [Hardware](), [Open Source]() Tags: [accelerate](), [compression](), [cpu](), [cuda](), [cuda-x](), [data](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [databases](), [featured](), [gpu](), [hardware](), [memory](), [nvidia](), [nvlink](), [performance](), [sql]() ### 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... ## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Omniverse](), [Optimization](), [Tooling](), [GPU](), [Nvidia](), [CUDA](), [PyTorch](), [Physical AI](), [Robotics](), [Synthetic Data Generation](), [real-time](), [Reinforcement learning]() Tags: [3d](), [ai](), [autonomous](), [autonomous-vehicles](), [cuda](), [developer-tools](), [developer-tools-techniques](), [driving](), [engineering](), [featured](), [gpu](), [lidar](), [machine-learning](), [nvidia](), [omniverse](), [optimization](), [performance](), [physical-ai](), [real-time](), [reinforcement-learning](), [simulation-modeling-design](), [synthetic-data-generation]() ### AI overview This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance. ### Source excerpt NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such... ## Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop) DevFeed: [Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop)]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-06-22T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [DGX Spark](), [Ubuntu](), [NVIDIA DGX](), [GPU](), [NVLink](), [CUDA](), [Docker](), [Ansible](), [vllm](), [Blackwell](), [networking](), [GitHub]() Tags: [ai](), [ansible](), [blackwell](), [cuda](), [dell](), [dgx-spark](), [docker](), [github](), [gpu](), [homelab](), [networking](), [nvidia-dgx](), [nvlink](), [ubuntu](), [vllm]() ### AI overview A guide to replacing DGX OS with a minimized Ubuntu 24.04 Server installation on GB10 systems such as the NVIDIA DGX Spark and ASUS Ascent GX10. It explains the memory and power benefits of removing GNOME while retaining the NVIDIA drivers, CUDA, Docker, and NVIDIA Container Toolkit, and covers ConnectX-7 networking, dual-node setup, and Ansible automation. ### Source excerpt When you buy an NVIDIA DGX Spark or an ASUS Ascent GX10, it ships with DGX OS. DGX OS is NVIDIA's managed Ubuntu image, and it is fine - if you want a full GNOME desktop on an AI box. I did not want that. The GB10 has 128 GB of unified memory shared between the CPU and GPU over NVLink-C2C. Every gigabyte the OS and desktop environment consume is a gigabyte not available to your model. On the ... ## Updates on an Axon AI Role, an AI Slide Deck, and an AI Systems Course DevFeed: [Updates on an Axon AI Role, an AI Slide Deck, and an AI Systems Course]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-05-28T15:03:02Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Artificial Intelligence](), [Machine learning](), [CUDA](), [foundation-models](), [systems]() Tags: [ai](), [cuda](), [ml](), [updates]() ### AI overview The author shares updates on a new L8 SWE/AI role at Axon, an expanding slide deck about GPUs and AI, and an AI Systems course, with an open live Q&A session planned for the next day. ### Source excerpt Updates on my new L8 SWE/AI role at Axon, the AI Slide Deck, and the production-focused AI Systems course + an open live session tomorrow. ## Running the Latest vLLM on the NVIDIA DGX Spark DevFeed: [Running the Latest vLLM on the NVIDIA DGX Spark]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [vllm](), [DGX Spark](), [Docker](), [Dockerfile](), [GitHub Actions](), [NCCL](), [CUDA](), [PyTorch](), [GitHub](), [NGC](), [GPU](), [Ubuntu]() Tags: [ai](), [cuda](), [dgx-spark](), [docker](), [docker-image](), [github](), [github-actions](), [gpu](), [homelab](), [nccl](), [ngc](), [nvidia-dgx](), [pytorch](), [self-hosted](), [ubuntu](), [vllm]() ### AI overview This tutorial presents a reproducible Docker image pipeline for running current vLLM releases on the NVIDIA DGX Spark's GB10 ARM64 platform. It explains why NVIDIA's NGC images can lag upstream and describes a GitHub Actions build on a self-hosted Spark runner with pinned inputs and generated lockfiles. ### Source excerpt When I built my local AI cluster on a pair of ASUS Ascent GX10s, the hard part was not serving a model. The hard part was getting a working vLLM image with current components. NVIDIA's official image was already over a month behind by the time I needed it, and waiting on their release schedule was not an option. If you saw that post, you know the GX10 is an ARM64 machine built around NVIDIA's ... ## Cursor's Agent-Written CUDA Kernels, Claude Cowork for Non-Engineers, and Stanford's Frontier Systems - 📚 The Tokenizer Edition #24 DevFeed: [Cursor's Agent-Written CUDA Kernels, Claude Cowork for Non-Engineers, and Stanford's Frontier Systems - 📚 The Tokenizer Edition #24]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-04-16T12:03:16Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [CUDA](), [cursor](), [rlvr](), [multimodal](), [Large Language Model](), [Learning]() Tags: [ai](), [ai-ml](), [claude](), [cuda](), [cursor](), [llm](), [multimodal](), [rlvr](), [tutorial]() ### AI overview The Tokenizer Edition #24 is a curated AI/ML newsletter covering agent-written CUDA kernels at Cursor, RLVR training research, multimodal retrieval resources, AI infrastructure learning, and selected videos, tools, and papers. ### Source excerpt This week's most valuable AI resources ## GPU-Accelerated Remote Desktop on Linux from macOS - the Hard Way DevFeed: [GPU-Accelerated Remote Desktop on Linux from macOS - the Hard Way]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-04-13T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [GPU](), [Linux](), [Ubuntu](), [macOS](), [Nvidia](), [CUDA](), [Blackwell](), [Latency](), [Microsoft]() Tags: [blackwell](), [cuda](), [gpu](), [homelab](), [latency](), [linux](), [macos](), [microsoft](), [nvidia](), [ubuntu]() ### AI overview This article documents the troubleshooting process for achieving GPU-accelerated H264 encoding over RDP from Ubuntu 24.04 ARM64 systems with NVIDIA GB10 Grace-Blackwell hardware to macOS. It compares xrdp's software-rendering path with GNOME Remote Desktop's CUDA-backed encoding and describes connection problems encountered with Microsoft's Windows App on macOS. ### Source excerpt What started as "just set up RDP" turned into an all day rabbit hole about how Linux remote desktop actually works, why most of it doesn't work with NVIDIA on ARM64, and what it actually takes to get GPU-accelerated H264 encoding over RDP from an Ubuntu machine to a Mac. What I was trying to do I have two ASUS Ascent GX10 machines running Ubuntu 24.04. They are ARM64 systems built around th... ## GTX 1080 Ti for Local LLM DevFeed: [GTX 1080 Ti for Local LLM]() Original publisher: [Read original article]() Published: 2026-02-28T23:33:54Z Content type: article Language: en Sources: [Ariya Hidayat]() Topics: [GPU](), [LLMs](), [llama.cpp](), [Nvidia](), [CUDA](), [Inference Performance](), [Retrieval Augmented Generation (RAG)]() Tags: [cmake](), [command-line](), [cuda](), [gcc](), [gemma](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [nvidia]() ### AI overview This article evaluates the eight-year-old NVIDIA GTX 1080 Ti as an affordable option for running local LLMs. It explains how its 11 GB of VRAM can support quantized models and relevant context windows, then describes measuring prompt-processing and token-generation performance with CUDA-enabled llama.cpp. ### Source excerpt Despite being over eight years old, the NVIDIA GTX 1080 Ti remains a compelling choice for enthusiasts keen on running LLM locally. ## Custom Kernels for All from Codex and Claude DevFeed: [Custom Kernels for All from Codex and Claude]() Original publisher: [Read original article]() Author: ben burtenshaw; Sayak Paul; Aritra Roy Gosthipaty; shaun smith Published: 2026-02-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [CUDA](), [Agent Skills](), [AI-assisted coding](), [PyTorch](), [diffusers](), [Large Language Model](), [GPU](), [kernel-builder]() Tags: [agent-skills](), [agents](), [benchmarks](), [claude-code](), [community](), [cuda](), [cursor](), [diffusers](), [gpu](), [kernels](), [memory](), [open-source](), [pytorch](), [transformers]() ### AI overview The article describes an agent skill that teaches coding agents to write production CUDA kernels. Claude and Codex were used to create working kernels for a diffusers pipeline and a transformers model, including PyTorch bindings and benchmarks. The skill packages GPU architecture, memory optimization, project structure, and integration expertise for on-demand use. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## We Got Claude to Build CUDA Kernels and teach open models! DevFeed: [We Got Claude to Build CUDA Kernels and teach open models!]() Original publisher: [Read original article]() Author: ben burtenshaw; shaun smith; merve; Pedro Cuenca Published: 2026-01-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [CUDA](), [Agent Skills](), [Claude Code](), [AI Development](), [Open Source Models & Datasets](), [AI Chat]() Tags: [agent-skills](), [agentic](), [claude](), [claude-code](), [code](), [coding](), [community](), [cuda](), [open-source](), [upskill]() ### AI overview This article explains how to use Claude and agent skills to improve smaller open-source models on difficult, domain-specific tasks. It demonstrates the process by generating, refining, and evaluating skills for writing CUDA kernels, using interactive traces and performance benchmarks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Upcoming Livestream: GPUs for AI (Shaped by You) DevFeed: [Upcoming Livestream: GPUs for AI (Shaped by You)]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-01-27T09:30:49Z Content type: article Language: en Sources: [Neural Bits]() Topics: [GPU](), [Artificial Intelligence](), [Hardware](), [CUDA](), [pcie](), [Nvidia](), [rocm](), [MLX](), [TensorRT](), [Google](), [groq]() Tags: [ai](), [cuda](), [google](), [gpu](), [groq](), [hardware](), [mlx](), [nvidia](), [pcie](), [rocm](), [tensorrt]() ### AI overview An upcoming livestream will discuss how GPUs and other accelerators support AI workloads. The author invites audience feedback to shape coverage of GPU hardware, PCIe, CUDA, ASICs, TPUs, LPUs, model optimization, and related technologies. ### Source excerpt You can choose the topics for a Live Session on GPUs in AI ## Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026 DevFeed: [Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026]() Original publisher: [Read original article]() Published: 2025-11-29T00:41:00Z Content type: opinion Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Artificial Intelligence](), [Machine learning](), [CUDA](), [OpenCL](), [Programming](), [NumPy]() Tags: [2026](), [ai](), [ai-ml](), [algebra](), [apple](), [clojure](), [cuda](), [linear](), [matrices](), [neanderthal](), [opencl](), [programming](), [vectors]() ### AI overview The article outlines a 2026 development and funding plan for the Uncomplicate ecosystem of Clojure libraries for AI, machine learning, and high-performance computing. It describes support for Nvidia GPUs, Apple Silicon, CPUs, CUDA, OpenCL, and several planned library improvements. ### Source excerpt I've applied for Clojurists Together yearly funding in 2026. Here's my application. If you are a Clojurists Together member, and would like to see continued development in this area, your vote can help me keep working on this :) My goal with this funding in 2026 is to continuously develop Clojure AI, ML, and high-performance ecosystem of Uncomplicate libraries (Neanderhal and many more), on Nvidia GPUs, Apple Silicon, and traditional PC. In this year, I will also focus on writing tutorals on my blog and creating websites for the projects involved, which is something that I wanted for years, but didn't have time to do because I spent all time on programming. How that work will benefit the Clojure community This will highly benefit the Clojure community as this is THE AI ecosystem for Clojure, and supporting AI is arguably the main focus on probably all software platforms. Clojure has something to offer on that front, beyond just calling OpenAI API as a web service! Uncomplicate grew to quite a few libraries (of which some are quite big; just Neanderthal is 28,000 lines of highly-condensed, aggresively macroized, and reusable code): Diamond ONNX Runtime, Neanderthal, Deep Diamond, ClojureCUDA, ClojureCPP, Apple Presets, ClojureCL, Fluokitten, Bayadera, Clojure Sound, and Commons. Here's a word or two of how I hope to improve each of these libraries with Clojurists Together funding in 2026. Neanderthal (Clojure's alternative to NumPy, on steroids) In 2025, Neanderthal celebrated its 10th birthday. It started as a humble but fast matrix and vector library for Clojure, but after 10 years of relentless improvements, now it boasts a general matrix/vector/linear algebra API implemented by no less than 5(!) engines for CPUs, GPU (Nvidia CUDA), GPU (OpenCL: AMD, Intel, Nvidia), Apple Silicon (Accelerate), and general CPU (OpenBLAS). And this is not a superficial support for the sake of ticking a check box; each of these engines support much more operations on exotic structure [Next page]()