# Compute Published articles for Compute. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Secure Compute and Static IP builds start 64% faster DevFeed: [Secure Compute and Static IP builds start 64% faster]() Original publisher: [Read original article]() Author: Karim Hasebou Published: 2026-09-16T17:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Deployment](), [Containers](), [Network Configuration]() Tags: [automatically](), [build](), [compute](), [containers](), [faster](), [ip](), [network-configuration](), [static]() ### AI overview Vercel reports that builds using Secure Compute or Static IPs now start 64% faster on average, improving from 6.7 seconds to 2.4 seconds. The change uses prewarmed build containers with network configuration attached at build start and requires no configuration changes. ### Source excerpt Builds using Secure Compute or Static IPs now start 64% faster, with the average time from deployment creation to build start dropping from 6.7 seconds to 2.4 seconds. Previously, each build waited for a new build container to boot with its network configuration. These builds now use prewarmed build containers, with your network configuration attached when the build starts. The improvement is applied automatically to builds using Secure Compute or Static IPs, with no configuration changes required. Learn more about Secure Compute and Static IPs. Read more ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production DevFeed: [From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production]() Original publisher: [Read original article]() Author: Vishal Ganeriwala Published: 2026-09-15T16:55:59Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [AI Factory](), [NVIDIA DSX](), [datacenter](), [Orchestration](), [Inference](), [High-Performance Computing]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [capacity](), [compute](), [dsx](), [efficiency](), [inference](), [nvidia](), [orchestration](), [production]() ### AI overview The article describes how Emerald AI's Conductor platform responds to utility demand signals by adjusting flexible data-center workloads while keeping high-priority AI inference running. It also reports that Lambda's validation found a fixed power budget could support 24% more token throughput when managed intelligently. ### Source excerpt On a sweltering August evening in Silicon Valley, as the sun dropped and air conditioning loads spiked, Silicon Valley Power sent a signal to an AI factory to adjust its power consumption. Varun Sivaram was watching on Zoom with about forty others -- his team at Emerald AI in their San Francisco conference room, engineers [...] ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Federated Learning](), [Docker](), [Kubernetes](), [Nvidia](), [Server](), [dataset](), [Deployment]() Tags: [architecture](), [compute](), [configuration](), [container](), [data-analytics-processing](), [data-science](), [dataset](), [deployment](), [docker](), [docker-container](), [federated-learning](), [gpu](), [job](), [kubernetes](), [nvidia](), [nvidia-flare](), [server]() ### AI overview This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling. ### Source excerpt Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the... ## Infineon RISC-V for Automotive at Hot Chips 2026 DevFeed: [Infineon RISC-V for Automotive at Hot Chips 2026]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-13T21:58:44Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [RISC-V](), [Microcontroller](), [real-time](), [Low Latency](), [Architecture & Design](), [Security](), [IO](), [Inference Performance]() Tags: [2026](), [architecture](), [automotive](), [compute](), [cost](), [efficiency](), [infineon](), [latency](), [low-latency](), [microcontrollers](), [mobile](), [performance](), [posix](), [processors](), [real-time](), [risc-v](), [security]() ### AI overview Infineon presents RISC-V automotive processors for next-generation vehicle architectures. The article covers real-time control, low latency, power efficiency, security, zone controllers, central car computers, and heterogeneous workloads including DSP, AI inference, audio, and POSIX-based services. ### Source excerpt At Hot Chips 2026, Infineon presented a case for using RISC-V in various automotive processors in next-generation cars The post Infineon RISC-V for Automotive at Hot Chips 2026 appeared first on ServeTheHome. ## Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack DevFeed: [Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-12T18:24:22Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AWS Outposts](), [Amazon Web Services](), [cloud-infrastructure](), [Network](), [Latency](), [data-processing](), [Security]() Tags: [5g](), [automation](), [aws](), [aws-outposts](), [cloud](), [compute](), [connectx](), [data-processing](), [enterprise](), [governance](), [latency](), [low-latency](), [networking](), [security]() ### AI overview AWS has generally released a second-generation single-rack AWS Outposts configuration: a self-contained 42U rack combining compute, storage, and networking with up to 2,688 vCPUs and 100 TB of Amazon EBS. The article describes its on-premises cloud compatibility, compact footprint, supported instance families, and accelerated networking options for trading floors and 5G cores. ### Source excerpt AWS has made second-generation single-rack AWS Outposts generally available, a self-contained 42U rack that puts compute, storage, and networking together with up to 2,688 vCPUs and 100 TB of Amazon EBS. It runs the same APIs, console, automation, governance policies, and security controls as the multi-rack second-generation Outposts and the parent AWS Region, so an The post Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack appeared first on StorageReview.com. ## How to use Google microbenchmarks for evaluating TPU performance DevFeed: [How to use Google microbenchmarks for evaluating TPU performance]() Original publisher: [Read original article]() Author: Junjie Qian; Chi Shuen Lee; Yu-Hsuan (Amy) Lin; Haixiong (Sean) Wang Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Google AI](), [Inference Performance]() Tags: [architecture](), [benchmarks](), [compute](), [developers](), [google](), [guides](), [hardware](), [how-to](), [kernel](), [machine-learning](), [memory](), [mesh](), [metrics](), [model](), [open](), [open-source](), [performance](), [scale](), [software](), [tpu]() ### AI overview A tutorial on using Google's TPU microbenchmark suite to measure network, compute, memory, host-transfer, and attention performance. The results can establish a Roofline baseline and guide workload-specific optimization. ### Source excerpt Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound. This empirical baseline directly guides targeted software optimizations--such as kernel tuning, mesh sharding, and rematerialization--to maximize hardware utilization for large-scale model deployments. ## OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates DevFeed: [OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T21:27:42Z Content type: news Language: en Sources: [The New Stack]() Topics: [Inference](), [long-context]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [cloud-services](), [codex](), [compute](), [developers](), [inference](), [openai](), [research](), [sandbox](), [tools]() ### AI overview OpenAI's public-beta Agents API lets developers run long-lived agents with managed job state, context compression, optional tools, parallel subagents, and execution in OpenAI's sandbox or developer-controlled infrastructure. The article highlights the resulting inference and compute costs, citing internal research-agent usage figures. ### Source excerpt OpenAI rolled out its Agents API in public beta Thursday, opening the backend behind Codex to developers looking to run The post OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates appeared first on The New Stack. ## NVIDIA Personal AI Router Distributes AI Tasks across Local Compute DevFeed: [NVIDIA Personal AI Router Distributes AI Tasks across Local Compute]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-09-11T15:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Local AI](), [Inference](), [GPU](), [Hardware]() Tags: [agent](), [agents](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [compute](), [demo](), [development](), [gpu](), [inference](), [large-language-models](), [local](), [ml-data-engineering](), [news](), [node](), [nvidia](), [nvidia-pair-ai-task-router](), [open-source](), [orchestration](), [performance]() ### AI overview NVIDIA has introduced PAIR in beta, a local router that distributes inference requests across compatible computers for multi-agent AI workloads. It works with local inference services such as Ollama and LM Studio and selects a node based on model and engine requirements. ### Source excerpt NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU. By Sergio De Simone ## DeepSeek's new model sets a template for powerful LLMs that run lean DevFeed: [DeepSeek's new model sets a template for powerful LLMs that run lean]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-11T07:15:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [deepseek](), [Large Language Model](), [Caching](), [Latency](), [Google]() Tags: [ai-and-ml](), [cache](), [compute](), [cost](), [datacenter](), [deepseek](), [embedding](), [flash](), [google](), [gpu](), [latency](), [llm](), [llms](), [model](), [release]() ### AI overview DeepSeek V4.1 Flash is a larger LLM whose architectural changes aim to reduce serving memory and compute needs. The article highlights lower KV-cache consumption, improved prompt processing, and an N-gram-based conditional memory module. ### Source excerpt DeepSeek V4.1 Flash proves that just because you build a bigger model doesn't mean you need more GPUs to serve it ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. DevFeed: [Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-10T19:37:30Z Content type: news Language: en Sources: [The New Stack]() Topics: [Model Development](), [Training AI Models](), [anthropic]() Tags: [ai](), [compute](), [infrastructure](), [models](), [news](), [open](), [training]() ### AI overview Mistral's EUR 3 billion Series D is presented as a bet that open-weight AI needs accompanying compute and infrastructure to reduce dependence on concentrated model and chip providers. ### Source excerpt This week, Mistral announced it raised EUR 3 billion in a Series D funding round, pushing its post-money valuation past EUR 21 The post Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. appeared first on The New Stack. ## Announcing On-Demand Compute: Instant compute for your most intensive workloads DevFeed: [Announcing On-Demand Compute: Instant compute for your most intensive workloads]() Original publisher: [Read original article]() Author: Melvyn Peignon Published: 2026-09-10T14:22:58Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [autoscaling](), [cloud-infrastructure]() Tags: [autoscaling](), [clickhouse](), [cloud](), [compute](), [cost](), [infrastructure](), [production](), [scale](), [workers]() ### AI overview ClickHouse announces a private preview of On-Demand Compute, which allocates shared workers to eligible individual queries so intensive workloads can run without competing with a service's primary compute. ### Source excerpt ClickHouse On-Demand Compute lets you scale individual queries with additional workers, run intensive workloads without disrupting production, and use compute when you need it. ## From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry DevFeed: [From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [datacenter](), [networking](), [Software](), [data]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [blackwell](), [compute](), [cuopt](), [data-center](), [gb200](), [infrastructure](), [llms](), [manufacturing](), [memory](), [nemo](), [nemotron](), [networking](), [nvidia](), [nvidia-blackwell](), [nvl72](), [performance](), [production](), [real-time](), [software](), [supply-chain](), [vera-rubin]() ### AI overview NVIDIA describes how it measures and reduces the time from wafer-out to first token across complex Grace Blackwell and Vera Rubin supply chains. The article focuses on time-to-rack, critical material allocation, real-time visibility, redundancy, reliability, and codifying human expertise. ### Source excerpt NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two... ## Vercel Sandbox is now available in all regions DevFeed: [Vercel Sandbox is now available in all regions]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [cloud-infrastructure](), [Latency](), [Command-line interface](), [SDKs](), [configuration]() Tags: [cli](), [compute](), [configuration](), [cpu](), [enterprise](), [latency](), [memory](), [sdk](), [support](), [update](), [vercel]() ### AI overview Vercel Sandbox is now available across all 20 Vercel compute regions, expanding from four. Users can select primary and failover regions, helping reduce latency and meet data residency requirements. Region selection is available on every plan, while Pro and Enterprise teams can configure failover regions. ### Source excerpt Vercel Sandbox can now run in all 20 Vercel compute regions, up from four. Running sandboxes closer to the databases, storage, and other services they access reduces latency. Teams can also keep sandbox workloads in approved regions to support data residency and regional processing requirements. iad1 remains the default region. Region selection is available on every plan. Pro and Enterprise teams can also configure failover regions. If Vercel can't create a sandbox in the primary region, it tries each configured failover region in order. Teams with data residency requirements can limit primary and failover regions to approved geographies. Set a default region for new sandboxes from your project's Settings > Sandboxes. With the SDK: With the CLI: A region passed at creation overrides the project default. Existing sandboxes remain in the regions where they were created. Active CPU and Provisioned Memory rates vary by region. See Sandbox pricing. If you use the Sandbox SDK or CLI, update it to the latest version before selecting one of the newly supported regions. Learn about region selection, failover, and CLI configuration in the Sandbox regions documentation. Read more ## ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap DevFeed: [ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-09-10T00:00:00Z Content type: comparison Language: en Sources: [ClickHouse Blog]() Topics: [data](), [cloud-infrastructure]() Tags: [analytics](), [architecture](), [clickhouse](), [cloud](), [compute](), [cost](), [data](), [infrastructure](), [latency](), [performance](), [real-time](), [sorting](), [storage](), [sync]() ### AI overview A comparison of ClickHouse Cloud and Snowflake for a continuous real-time analytics workload, examining how ingestion and pre-aggregation affect freshness, query work, latency, and cost. ### Source excerpt ClickHouse Cloud delivered 412x better performance per dollar than Snowflake in CostBench. We trace the gap from fresh data arriving to fast answers coming back. ## Samsung to help fortify OpenAI's semiconductor supply chain DevFeed: [Samsung to help fortify OpenAI's semiconductor supply chain]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-09T20:18:35Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [samsung]() Tags: [ai](), [compute](), [datacenter](), [memory](), [openai](), [samsung](), [supply-chain](), [systems](), [tsmc]() ### AI overview Samsung is positioned to help strengthen OpenAI's semiconductor supply chain, with support spanning compute and memory. ### Source excerpt Semiconductor supply chains are hard, but Samsung offers OpenAI relief in many forms spanning compute and memory ## Arm Neoverse CSS N4 Launched for Next-Gen CPUs and DPUs DevFeed: [Arm Neoverse CSS N4 Launched for Next-Gen CPUs and DPUs]() Original publisher: [Read original article]() Author: Cliff Robinson Published: 2026-09-09T17:10:12Z Content type: news Language: en Sources: [ServeTheHome]() Topics: [Arm](), [cpu](), [datacenter]() Tags: [architecture](), [arm](), [armv9](), [compute](), [connectivity](), [core](), [cpu](), [ddr5](), [memory](), [mesh](), [neoverse](), [neoverse-css](), [neoverse-n4](), [pcie](), [server](), [server-cpus]() ### AI overview Arm announced the Neoverse CSS N4 compute subsystem for future server CPUs and DPUs. The 3nm design supports 8 to 128 cores per die, Armv9.3, PCIe Gen7, LPDDR6, DDR5, MRDIMMs, and chiplet connectivity. ### Source excerpt The new Arm Neoverse CSS N4 IP is out so companies can quickly build next-generation power-efficient PCIe Gen7 CPUs The post Arm Neoverse CSS N4 Launched for Next-Gen CPUs and DPUs appeared first on ServeTheHome. ## Built for agents: Omarchy's pipeline moves to DigitalOcean DevFeed: [Built for agents: Omarchy's pipeline moves to DigitalOcean]() Original publisher: [Read original article]() Author: Paddy Srinivasan Published: 2026-09-09T15:30:15Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Linux](), [Open Source](), [Maintainers](), [Pull Request](), [Testing]() Tags: [agentic](), [agents](), [compute](), [digitalocean](), [infrastructure](), [linux](), [maintainers](), [news](), [open](), [open-source](), [qa](), [review](), [upgrade]() ### AI overview DigitalOcean is becoming the agentic compute provider for Omarchy, a keyboard-first Linux desktop built on Arch and Hyprland. Omarchy's production pipeline, packaging builds, pull request review agents, and QA testing now run on DigitalOcean infrastructure. ### Source excerpt Omarchy is a keyboard-first Linux desktop built on Arch and Hyprland by David Heinemeier Hansson (DHH), and its production pipeline, packaging builds, PR review agents, and QA testing, now run on DigitalOcean. Agent workloads don't behave like a typical web server. They're bursty and parallel: an agent spins up, does one job, and shuts down. Omarchy's pipeline is a real example of that pattern, and DigitalOcean infrastructure complements its needs. A single doctl command can provision a Droplet, run the job, call separately configured inference services, and destroy the Droplet when the work is done. Alongside the infrastructure move, DigitalOcean is joining the Omacom Foundation, the nonprofit that funds Omarchy's infrastructure and the open source projects it depends on, as a Founding Corporate Patron, and will serve as Omarchy's agentic compute provider. "I'm thrilled to have DigitalOcean become a Founding Corporate Patron of the Omacom Foundation, and our new agentic compute provider as well. We have such grand ambitions for Omarchy, and it's time we upgrade our technical infrastructure to match. Whether it's build servers for packaging, agent runners for PR reviews, or Droplets for QA testing, DigitalOcean simply has everything we need. They also have the right mindset for the future and our new age of agents. Couldn't imagine a better fit!" -- David Heinemeier Hansson, creator of Omarchy What's Next Coming soon, DigitalOcean and Omarchy plan to release a 1-click Omarchy Droplet available through the DigitalOcean Marketplace, enabling developers to quickly deploy the same environment used by the maintainers. ## Improving Lakebase Postgres Compute Cache on Neon, Part 1 DevFeed: [Improving Lakebase Postgres Compute Cache on Neon, Part 1]() Original publisher: [Read original article]() Author: Sunil Kamath Published: 2026-09-09T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Caching](), [Databases](), [Operating system](), [Amazon S3](), [cpu](), [Latency](), [autoscaling]() Tags: [autoscaling](), [cache](), [compute](), [cpu](), [engineering](), [latency](), [memory](), [os](), [postgres](), [s3](), [storage]() ### AI overview Neon describes a Lakebase Postgres compute-cache change that allocates most memory to shared buffers backed by huge pages. The goal is to keep hot pages in DRAM, reducing storage reads, CPU use, and latency; the article reports up to roughly 2x throughput on specified fixed-size computes. ### Source excerpt On large fixed-size Lakebase Postgres computes on Neon, we now put most of the machine's memory into Postgres shared buffers and back that cache with huge pages. Hot pages stay in DRAM instead of falling through to a local disk cache, so the same working set is served faster and with less CPU. ## China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes DevFeed: [China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:54:31Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [AI Platform](), [AI Inference](), [Training AI Models](), [kueue](), [Fine-tuning](), [Cloud Native Ecosystem](), [datacenter]() Tags: [ai](), [ai-infrastructure](), [ai-training](), [announcements](), [case-study](), [china](), [compute](), [cost](), [fine-tuning](), [inference](), [kubernetes](), [kueue](), [lora]() ### AI overview China Merchants Bank won a CNCF case-study contest for a Kubernetes-based AI platform that shares nearly 10,000 accelerator cards across training, fine-tuning, and online inference. The bank reports increased average accelerator utilization and lower inference costs. ### Source excerpt New cloud native platform lifted average accelerator compute utilization from 35% to more than 60% and cut inference cost per 1 million tokens by more than 60% Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon +... ## Pico Space Pro Appears To Receive FCC Certification Following Delay To Q4 DevFeed: [Pico Space Pro Appears To Receive FCC Certification Following Delay To Q4]() Original publisher: [Read original article]() Author: Luna Published: 2026-09-05T23:39:38Z Content type: news Language: en Sources: [UploadVR]() Topics: [Hardware](), [Software]() Tags: [battery](), [compute](), [fcc](), [headsets-tech](), [product-launch](), [regulatory](), [release](), [release-schedule](), [space]() ### AI overview Pico Space Pro, an upcoming mixed-reality headset, appears to have received FCC certification after its launch was postponed to the fourth quarter of 2026. The article reports that FCC filings reveal a tethered compute and battery puck. ### Source excerpt A new Pico device, highly likely the upcoming Space Pro, received FCC certification following a delay to Q4. Filings reveal a tethered compute and battery puck. [Next page]()