# accelerate Published articles for accelerate. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Use AI to Accelerate Delivery Without Lowering Software Quality DevFeed: [Use AI to Accelerate Delivery Without Lowering Software Quality]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-09-13T04:01:35Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [Software](), [Artificial Intelligence](), [coding](), [context]() Tags: [accelerate](), [ai](), [context](), [quality](), [software]() ### AI overview An opinion piece arguing that AI should speed delivery without reducing software-quality standards, using engineering judgment as scalable context, constraints, and checks. ### Source excerpt AI should accelerate delivery, not lower your standards. Turn engineering judgment into context, constraints, and checks that scale ## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-02T16:04:19Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Large Language Model]() Tags: [accelerate](), [agentic-ai-generative-ai](), [ai](), [ai-inference](), [ai-models](), [batch](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [inference](), [inference-performance](), [llm](), [llms](), [nvfp4](), [training-ai-models]() ### AI overview The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula. ### Source excerpt This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and... ## Respond to security threats faster with Tines and Observability Pipelines DevFeed: [Respond to security threats faster with Tines and Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Security](), [observability](), [Automation](), [log management](), [Orchestration](), [API]() Tags: [accelerate](), [api](), [automation](), [devsecops](), [logs](), [observability-pipelines](), [orchestration](), [real-time](), [reduce](), [security](), [workflows]() ### AI overview Tines and Datadog Observability Pipelines automate security-log processing by standardizing and routing logs, updating pipelines through APIs and reference tables, and applying current context in real time. The integration helps reduce alert noise, identify access-control gaps and suspicious activity, and accelerate threat investigations. ### Source excerpt Learn how Tines workflows can update Datadog Observability Pipelines to prioritize threats, reduce alert noise, and accelerate investigations. ## NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure DevFeed: [NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure]() Original publisher: [Read original article]() Author: Farshad Ghodsian Published: 2026-08-26T21:06:58Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [AI Infrastructure](), [Nvidia](), [AI Platform](), [datacenter](), [Inference](), [systems]() Tags: [accelerate](), [agentic-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [architecture](), [compute](), [data-center](), [data-center-cloud](), [deployment](), [inference](), [infrastructure](), [integration](), [memory](), [model](), [models](), [networking-communications](), [nvidia](), [nvlink](), [performance](), [platform](), [scale](), [support](), [top-stories]() ### AI overview NVIDIA NVLink Fusion connects custom XPUs and CPUs to NVIDIA's AI infrastructure platform, while NVHBM provides validated HBM base-die technology intended to increase memory bandwidth, save package area, and reduce power consumption. The article describes benefits for training and large-scale inference, including up to 30% more memory bandwidth per stack than standard HBM4e. ### Source excerpt AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads,... ## CircleCI Smarter Testing: Stop running tests that don't matter DevFeed: [CircleCI Smarter Testing: Stop running tests that don't matter]() Original publisher: [Read original article]() Author: Nathan Fish Published: 2026-08-26T19:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Testing](), [CI/CD]() Tags: [accelerate](), [auto-rerun-failed-tests](), [ci-cd](), [circleci](), [circleci-news](), [developer-productivity](), [dynamic-test-splitting](), [engineering-productivity](), [flaky](), [intelligent-test-selection](), [parallel](), [pipeline](), [quality](), [reduce](), [smarter-testing](), [test-impact-analysis](), [testing](), [tests](), [time]() ### AI overview CircleCI describes Smarter Testing, a set of features designed to reduce CI/CD test execution time by skipping tests unaffected by changes, balancing parallel nodes, and retrying flaky tests. The article says early users have seen test runs up to four times faster. ### Source excerpt Testing eats up to half your pipeline time. See how CircleCI Smarter Testing skips unaffected tests, rebalances parallel nodes, and retries flaky tests. ## The search multiplier: Driving revenue, productivity, and AI at scale DevFeed: [The search multiplier: Driving revenue, productivity, and AI at scale]() Original publisher: [Read original article]() Author: Nicole Volk Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [AI search](), [AI Infrastructure](), [Generative AI](), [idc](), [Resilience]() Tags: [accelerate](), [agentic-ai-cloud-search-context-engineering-customer-experience-deployment-end-user-experience](), [ai](), [ai-infrastructure](), [analysis](), [business-value](), [customer-story-elasticsearch-workplace-search](), [elasticsearch](), [elasticsearch-platform](), [financial-services](), [government](), [healthcare](), [resilience](), [retail](), [retrieval](), [search]() ### AI overview An IDC study of 11 large enterprises reports that the Elasticsearch Platform, used as a search and retrieval foundation, helped organizations deliver AI products faster while improving revenue, productivity, search relevance, and operational resilience. The study reports a 517% three-year ROI, $13.4 million in annual benefits per organization, and an 11-month payback period. ### Source excerpt An independent IDC study found organizations deploying the Elasticsearch Platform for enterprise search and agentic AI deliver AI products faster, drive higher revenue, boost productivity, and strengthen operational resilience across the business. ## 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]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### 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... ## WeatherNext: AI model achieves breakthrough in forecasting cyclones DevFeed: [WeatherNext: AI model achieves breakthrough in forecasting cyclones]() Original publisher: [Read original article]() Author: WeatherNext team Published: 2026-08-06T15:06:15Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Open Source Models & Datasets](), [Google](), [data](), [Code]() Tags: [accelerate](), [ai](), [code](), [community](), [data](), [google](), [infrastructure](), [model](), [models](), [open](), [performance](), [research](), [science](), [speed](), [tpu]() ### AI overview WeatherNext is an AI weather-forecasting model that uses Functional Generative Networks to produce large ensembles of predictions and capture uncertainty. It generates 15-day forecasts in under a minute on a TPU, supports cyclone forecasting at relatively coarse resolution, and is being open sourced with its code and model weights for research and operational use. ### Source excerpt WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model. ## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-04T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [foundation-models](), [Cosmos](), [model-deployment](), [Reinforcement learning](), [Inference](), [GitHub](), [hugging face](), [Open Source Models & Datasets]() Tags: [accelerate](), [automotive-transportation](), [autonomous](), [benchmarks](), [cosmos](), [customization](), [data](), [developer-tools-techniques](), [developers](), [development](), [diffusion](), [drive](), [driving](), [evaluation](), [featured](), [fine-tuning](), [generate](), [generation](), [github](), [hugging-face](), [inference](), [learning](), [models](), [nvidia](), [reasoning](), [robot-navigation](), [robotics](), [simulation-modeling-design](), [top-stories]() ### AI overview NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows. ### Source excerpt Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data... ## IBM commits $50M in quantum access for US Genesis Mission DevFeed: [IBM commits $50M in quantum access for US Genesis Mission]() Original publisher: [Read original article]() Published: 2026-07-22T20:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Artificial Intelligence](), [Quantum Computing](), [High-Performance Computing](), [Supercomputing]() Tags: [accelerate](), [ai](), [department-of-energy](), [discovery](), [government](), [high-performance-computing](), [ibm](), [news](), [project](), [quantum](), [supercomputing](), [us]() ### AI overview IBM says a project was selected by the U.S. Department of Energy's Genesis Mission to accelerate AI-driven scientific discovery and will contribute up to $50 million in quantum system access. The mission combines AI, quantum computing, supercomputing, and scientific instruments. ### Source excerpt An IBM project was also selected to accelerate AI-driven quantum application discovery. ## Advancing the next era of national science DevFeed: [Advancing the next era of national science]() Original publisher: [Read original article]() Published: 2026-07-22T12:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Frontier AI](), [OpenAI](), [API](), [codex](), [coding](), [Cybersecurity]() Tags: [accelerate](), [ai](), [api](), [codex](), [coding](), [cybersecurity](), [department-of-energy](), [energy](), [frontier-ai](), [global-affairs](), [government](), [gpt](), [infrastructure](), [innovation](), [models](), [openai](), [research](), [science](), [systems](), [tools](), [workflows]() ### AI overview OpenAI describes commitments to support the U.S. Department of Energy's Genesis Mission by providing frontier AI, Codex access, API support, specialized bioscience capabilities, model access, and cyber capabilities to researchers at national laboratories and universities. The initiative aims to accelerate scientific discovery and strengthen research infrastructure. ### Source excerpt OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier AI to accelerate discovery. ## Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI DevFeed: [Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI]() Original publisher: [Read original article]() Author: Eduardo Alvarez Published: 2026-07-21T18:15:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Nvidia](), [Artificial Intelligence](), [Inference](), [Inference Performance](), [long-context](), [datacenter](), [Transformer](), [Deployment]() Tags: [accelerate](), [agentic](), [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-factory](), [ai-inference](), [architecture](), [blackwell](), [cache](), [compute](), [data-center](), [data-center-cloud](), [developer-tools-techniques](), [dsx](), [efficiency](), [featured](), [gpu](), [inference](), [inference-performance](), [latency](), [long-context](), [memory](), [mixture-of-experts-moe](), [nvfp4](), [nvidia](), [rubin-gpu](), [scale](), [tensor-cores](), [top-stories](), [training](), [vera-rubin]() ### AI overview This article examines the NVIDIA Rubin GPU architecture and its co-designed Vera Rubin platform for agentic AI inference. It describes how Tensor Cores, HBM4 memory, the Transformer Engine, NVFP4 performance, cache, decoding, and scale-up systems address throughput, latency, long-context execution, and rack-scale deployment. ### Source excerpt What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale.... ## Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField DevFeed: [Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-16T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [AI Factory](), [cloud-infrastructure](), [GPU](), [Inference](), [Network](), [networking](), [Security](), [telemetry](), [systems](), [NVIDIA Research]() Tags: [accelerate](), [agent](), [agentic-ai](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [ai-factory](), [architecture](), [compute](), [cpu](), [data-center](), [data-center-cloud](), [dsx](), [featured](), [gpu](), [infrastructure](), [latency](), [memory](), [network](), [networking](), [networking-communications](), [nvidia](), [policy](), [storage](), [tool](), [vera-cpu](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview This article explains how agentic AI and long-context inference create demanding data-path requirements for AI factories. It describes NVIDIA BlueField-4, Vera BlueField-4 STX, and DOCA as infrastructure technologies that offload, accelerate, and isolate networking, storage, security, telemetry, and control-plane services while improving utilization, latency, isolation, cost per token, and energy efficiency. ### Source excerpt Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage... ## Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode DevFeed: [Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-06-30T21:30:33Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [AWS CloudFormation](), [Deployment](), [Amazon Web Services](), [Infrastructure as code](), [Command-line interface](), [AWS Management Console](), [Resilience](), [SDKs](), [Amazon Simple Queue Service (SQS)](), [AWS Lambda](), [Kiro]() Tags: [accelerate](), [agents](), [ai-tools](), [amazon-simple-queue-service-sqs](), [aws](), [aws-cloudformation](), [aws-lambda](), [aws-management-console](), [cli](), [cloudformation](), [deployment](), [developers](), [infrastructure](), [kiro](), [launch](), [management-tools](), [news](), [resilience](), [sdks](), [testing]() ### AI overview AWS CloudFormation Express mode accelerates infrastructure deployments by completing when resource configuration is applied instead of waiting for extended stabilization checks. The article describes use in iterative development, production scenarios, testing, and AI-assisted infrastructure workflows, with examples involving SQS and Lambda. ### Source excerpt AWS CloudFormation speeds up infrastructure deployment with Express mode, enabling AI agents and developers to receive deployment confirmation in seconds and iterate faster. Available in all commercial Regions at no additional cost. ## 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... ## Co-Scientist: A multi-agent AI partner to accelerate research DevFeed: [Co-Scientist: A multi-agent AI partner to accelerate research]() Original publisher: [Read original article]() Author: Co-Scientist team Published: 2026-05-12T14:40:07Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [AI research agents]() Tags: [accelerate](), [agent](), [ai](), [collaboration](), [gemini](), [research](), [science]() ### AI overview Introduces Co-Scientist, a collaborative multi-agent AI partner built with Gemini to help researchers accelerate scientific breakthroughs. ### Source excerpt Introducing Co-Scientist, a collaborative AI partner built with Gemini to help researchers accelerate scientific breakthroughs. ## Four ways Google Research scientists have been using Empirical Research Assistance DevFeed: [Four ways Google Research scientists have been using Empirical Research Assistance]() Original publisher: [Read original article]() Published: 2026-04-29T21:07:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Artificial Intelligence](), [Data Mining & Modeling](), [datasets](), [Benchmark](), [Monitoring](), [real-time]() Tags: [accelerate](), [ai](), [benchmark](), [data](), [data-mining-modeling](), [experiment](), [general-science](), [generate](), [generative-ai](), [go](), [google](), [insights](), [machine-intelligence](), [monitoring](), [real-time](), [research](), [science](), [software]() ### AI overview Google Research scientists are using Empirical Research Assistance (ERA) to develop expert-level empirical software and explore AI-assisted scientific discovery across epidemiology, geospatial analysis, cosmology, atmospheric monitoring, and neuroscience. The article highlights ERA's use in computational modeling, interpretable solutions, and real-time forecasts for COVID-19, influenza, and RSV. ### Source excerpt Data Mining & Modeling ## AI-generated synthetic neurons speed up brain mapping DevFeed: [AI-generated synthetic neurons speed up brain mapping]() Original publisher: [Read original article]() Published: 2026-04-16T12:18:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [Point cloud](), [Training AI Models](), [datasets](), [neuron](), [data]() Tags: [3d](), [accelerate](), [ai](), [classification](), [errors](), [general-science](), [generation](), [google](), [health-bioscience](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [mapping](), [model](), [models](), [neuron](), [partners](), [research](), [scale](), [science](), [synthetic](), [visualization]() ### AI overview Google Research describes how MoGen generates synthetic neuronal shapes to improve AI models that reconstruct brain wiring maps. Adding synthetic training examples reduced reconstruction errors by 4.4%, potentially saving 157 person-years of manual proofreading for a complete mouse brain. ### Source excerpt General Science ## Codex now offers more flexible pricing for teams DevFeed: [Codex now offers more flexible pricing for teams]() Original publisher: [Read original article]() Published: 2026-04-02T10:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [AI Chat]() Tags: [accelerate](), [ai](), [app](), [business](), [chatgpt](), [codex](), [engineering](), [enterprise](), [experiments](), [macos](), [product](), [scale](), [windows](), [work](), [workflows]() ### AI overview Codex introduces pay-as-you-go, Codex-only seats for ChatGPT Business and Enterprise teams, with token-based billing and no fixed seat fee. The update also lowers the annual price of ChatGPT Business seats and offers limited credits for eligible new Codex-only users. ### Source excerpt Codex now includes pay-as-you-go pricing for ChatGPT Business and Enterprise, providing teams a more flexible option to start and scale adoption. ## Removing supply chain friction: How PeopleTec improved developer productivity with Chainguard DevFeed: [Removing supply chain friction: How PeopleTec improved developer productivity with Chainguard]() Original publisher: [Read original article]() Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Security](), [supply-chain-security](), [chainguard containers](), [Containers](), [code productivity](), [migration]() Tags: [accelerate](), [chainguard](), [chainguard-assemble](), [chainguard-containers](), [chainguard-developer-experience](), [chainguard-migration](), [ci](), [code](), [containers](), [cve-remediation](), [developer](), [developer-velocity](), [migration](), [migration-guides](), [peopletec](), [provenance](), [security](), [supply-chain](), [vulnerability-management]() ### AI overview PeopleTec describes how it used Chainguard Security controls and Chainguard Containers to reduce software supply-chain friction, address vulnerabilities in base images, improve provenance and compliance workflows, and support developer productivity. The approach emphasized early adopters, low-friction migration, and automated policy checks in CI. ### Source excerpt Learn how PeopleTec used Chainguard to reduce security friction, accelerate adoption, and align platform consistency with developer velocity. ## Ulysses Sequence Parallelism: Training with Million-Token Contexts DevFeed: [Ulysses Sequence Parallelism: Training with Million-Token Contexts]() Original publisher: [Read original article]() Author: Kashif Rasul; Stas Bekman Published: 2026-03-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [long-context](), [GPU](), [hugging face](), [Open Source](), [trl]() Tags: [accelerate](), [ai](), [artificial-intelligence](), [distributed-training](), [ecosystem](), [gpu](), [guide](), [long-context](), [open-source](), [tokens](), [transformers](), [trl]() ### AI overview This article explains Ulysses Sequence Parallelism, a method for training transformer models with very long or million-token contexts by sharding sequences and partitioning attention heads across multiple GPUs. It describes the all-to-all communication steps and integration across the Hugging Face ecosystem, including Accelerate, Transformers Trainer, and TRL's SFTTrainer. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## RCCLX: Innovating GPU Communications on AMD Platforms DevFeed: [RCCLX: Innovating GPU Communications on AMD Platforms]() Original publisher: [Read original article]() Author: Sudharssun Subramanian; Subodh Iyengar; Cen Zhao; Srinath Bayareddy; James Hongyi Zeng Published: 2026-02-24T21:30:54Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [GPU](), [Meta](), [communications](), [AI Models](), [Latency](), [Inference](), [Large Language Model](), [Transformer]() Tags: [accelerate](), [ai](), [ai-models](), [ai-research](), [communications](), [data-center-engineering](), [gpu](), [inference](), [large-language-model](), [latency](), [layer](), [meta](), [ml-applications](), [networking-traffic](), [open-source](), [parallelism]() ### AI overview Meta describes the initial open-source release of RCCLX, an enhanced version of RCCL for AMD platforms integrated with Torchcomms. The article presents Direct Data Access algorithms and Low Precision Collectives, including approaches intended to reduce communication latency during large language model inference. ### Source excerpt We are open-sourcing the initial version of RCCLX - an enhanced version of RCCL that we developed and tested on Meta's internal workloads. RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend. Communication patterns for AI models are constantly evolving, as are hardware [...] Read More... The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta. ## Accelerating discovery in India through AI-powered science and education DevFeed: [Accelerating discovery in India through AI-powered science and education]() Original publisher: [Read original article]() Author: Demis Hassabis; Lila Ibrahim; Pushmeet Kohli Published: 2026-02-17T13:42:20Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [Frontier AI](), [Earth AI]() Tags: [accelerate](), [ai](), [collaboration](), [community](), [contests](), [earth-ai](), [frontier-ai](), [global](), [google](), [india](), [partnerships](), [research](), [responsibility-safety](), [science](), [training]() ### AI overview Google DeepMind is establishing a National Partnership for AI with Indian government bodies and local institutions to expand access to frontier AI capabilities for science and education. The collaboration with India's Anusandhan National Research Foundation includes access to AI models and tools, hackathons, community contests, training, and mentorship for students, researchers, and early-career professionals. ### Source excerpt Google DeepMind brings National Partnerships for AI initiative to India, scaling AI for science and education ## PVH reimagines the future of fashion with OpenAI DevFeed: [PVH reimagines the future of fashion with OpenAI]() Original publisher: [Read original article]() Published: 2026-01-27T06:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [OpenAI](), [data](), [Security & Privacy]() Tags: [accelerate](), [ai](), [chatgpt](), [creativity](), [data](), [design](), [efficiency](), [enterprise](), [global-affairs](), [innovation](), [openai](), [optimization](), [privacy](), [product](), [retail](), [scale](), [security](), [security-privacy](), [supply-chain]() ### AI overview PVH Corp. is adopting ChatGPT Enterprise and OpenAI frontier models across its global fashion operations. The initiative targets product design, demand planning, inventory optimization, supply-chain management, marketing, and consumer engagement, with an emphasis on data-driven decisions, creativity, efficiency, security, privacy, and responsible data governance. ### Source excerpt PVH Corp., parent company of Calvin Klein and Tommy Hilfiger, is adopting ChatGPT Enterprise to bring AI into fashion design, supply chain, and consumer engagement. [Next page]()