# AI Platform A computing platform that integrates tools and infrastructure for developing, deploying, and operating AI workloads. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing Amazon SageMaker HyperPod Inference Gateway DevFeed: [Introducing Amazon SageMaker HyperPod Inference Gateway]() Original publisher: [Read original article]() Author: Vinay Arora Published: 2026-09-18T13:08:34Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker HyperPod](), [Model Routing](), [model-serving](), [Inference Performance](), [Amazon Elastic Kubernetes Service](), [Kubernetes](), [AI Inference](), [GPU](), [AI Platform](), [Prometheus]() Tags: [amazon-eks](), [amazon-sagemaker-hyperpod](), [announcements](), [expert-400](), [gpu](), [inference](), [kubernetes](), [latency](), [metrics](), [model-serving](), [performance](), [routing]() ### AI overview Amazon SageMaker HyperPod Inference Gateway is a Kubernetes-native, GPU-aware routing add-on for Amazon EKS. It uses real-time GPU signals and model-serving metrics to route inference requests to suitable pods, aiming to reduce GPU waste and first-token latency without application changes. ### Source excerpt Amazon SageMaker HyperPod Inference Gateway is a Kubernetes-native, GPU-aware routing add-on for Amazon EKS. It uses real-time GPU signals to send each inference request to the best-suited pod, cutting first-token latency by up to 82% with no changes to your model servers or client applications. ## A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore DevFeed: [A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Shridhar Navanageri Published: 2026-09-17T15:41:03Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [AI Platform](), [observability](), [Authentication](), [Orchestration](), [scaling](), [control-plane](), [tracing](), [Self-hosted]() Tags: [agentic](), [agentic-ai](), [amazon-bedrock-agentcore](), [authentication](), [control-plane](), [customer-solutions](), [infrastructure](), [observability](), [orchestration](), [product](), [systems]() ### AI overview Wood Mackenzie built APEX, a shared agentic platform on Amazon Bedrock AgentCore, to help teams move AI agents from experimentation into production. The platform centralizes runtime orchestration, identity, observability, connectivity, safety, and persistent state so teams can focus on product-specific business logic. ### Source excerpt Wood Mackenzie built APEX, a shared agentic AI platform on Amazon Bedrock AgentCore so every team can ship production agents without rebuilding runtime, identity, observability, and guardrails from scratch. Learn why they chose AgentCore, how APEX Studio operates it, and where multi-agent systems go next. ## От Kubernetes-платформы к управлению гибридной распределённой инфраструктурой: встречайте Deckhouse Platform DevFeed: [От Kubernetes-платформы к управлению гибридной распределённой инфраструктурой: встречайте Deckhouse Platform]() Original publisher: [Read original article]() Author: Manassian (Флант) Published: 2026-09-17T10:04:13Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Kubernetes](), [on-prem](), [AI Platform](), [cloud-infrastructure](), [control-plane](), [GPU](), [PostgreSQL](), [clickhouse](), [airflow]() Tags: [airflow](), [clickhouse](), [control-plane](), [deckhouse](), [devops](), [gpu](), [kubernetes](), [on-prem](), [platform](), [postgresql](), [tag-82a17eb6b53d](), [tag-8b566f363766](), [tag-d57ff84cbcb7](), [tag-f7895e49ab8b]() ### AI overview The article announces Deckhouse Platform, which combines containerization, virtualization, and centralized cluster management. It describes ready-made infrastructure solutions for Kubernetes, virtual machines, combined workloads, AI workloads, data services, and private cloud deployments across cloud, on-premises, and edge environments. ### Source excerpt Мы объединили возможности контейнеризации, виртуализации и централизованного управления парком кластеров в один продукт -- Deckhouse Platform. Теперь контейнерами, виртуальными машинами, ИИ-нагрузками и сервисами данных можно управлять из одной точки, независимо от того, где они работают: в облаке, on-prem или на edge. В платформе уже есть готовые решения под типовые сценарии использования инфраструктуры, а под нестандартные можно собрать свою конфигурацию. Читать далее ## 9 top conversational AI platforms in 2026 DevFeed: [9 top conversational AI platforms in 2026]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-09-15T00:00:00Z Content type: comparison Language: en Sources: [Twilio Blog]() Topics: [Conversational AI](), [AI Platform](), [Deployment](), [Large Language Model](), [Messaging]() Tags: [ai-platform](), [article](), [conversational-ai](), [industry-insights](), [platforms]() ### AI overview A comparison of conversational AI platforms in 2026, covering hosted agent platforms, infrastructure and APIs, enterprise offerings, and open-source options. It explains how these products differ in model support, deployment ownership, channel coverage, integrations, and human handoff. ### Source excerpt Learn about the top conversational AI platforms in 2026, including infrastructure, enterprise, open source, and agentic options. See what fits your stack. ## Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure DevFeed: [Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AI Infrastructure](), [reliability](), [Orchestration](), [AI Platform]() Tags: [agents](), [ai](), [ai-infrastructure](), [announcements](), [funding](), [infrastructure](), [orchestration](), [outage](), [reliability](), [series](), [systems]() ### AI overview Temporal announces a $550 million Series E funding round at a $12.55 billion valuation. The company says the funding will support reliable infrastructure for long-running AI agents and applications, including orchestration and recovery across systems. ### Source excerpt AI is raising the bar for reliability. See why Temporal's $550M Series E, backed by Lightspeed and others, is built to meet that demand. ## How Tailscale built a customer-facing model router on AI Gateway DevFeed: [How Tailscale built a customer-facing model router on AI Gateway]() Original publisher: [Read original article]() Author: Susan Aziz Published: 2026-09-11T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [AI Platform](), [networking](), [API keys]() Tags: [ai-gateway](), [ai-models](), [api](), [model-routing](), [sandbox](), [security](), [vercel]() ### AI overview Tailscale describes using Vercel AI Gateway and Sandbox to deliver customer-facing access to hundreds of AI models through tailnet identity. The article explains that provider integration and secure agent execution were complex enough that Tailscale chose managed routing and sandboxing instead of building those layers in-house. ### Source excerpt Tailscale on Vercel Hundreds of AI models shipped to customers in-product Model access granted and revoked by tailnet network identity Went from model routing prototype to paying customers in months Tailscale connects a company's laptops, servers, cloud instances, and personal devices into one private network called a tailnet. Remy Guercio, who leads product for Aperture by Tailscale, describes it simply: "It's basically like a VPC that can span any cloud, on-prem, your house, and your phone." Aperture takes that same idea and applies it to AI. Instead of giving every employee, agent, or tool a separate provider API key, Aperture lets companies control model access through the tailnet itself. Add someone to the network, and they can immediately use approved models. Remove them, and access disappears. Under the hood, Aperture is built on Vercel AI Gateway and Vercel Sandbox. AI Gateway gives Tailscale one API for hundreds of models. Sandbox gives agents a safe place to run. Together, they let Tailscale offer model access and agent execution inside a customer's private network, without their team building every piece of AI infrastructure from scratch. Model routing is harder than it looks Tailscale is an infrastructure company, so building the routing and execution layers in-house was the obvious first option. But once they took a deeper look into the engineering effort required, they chose not to. The provider layer looked deceptively simple from the outside. "You would think all of the endpoints are the same," Remy says. "They are not." David Carney, Co-founder and Chief Strategy Officer, has the receipts, because Tailscale still maintains that plumbing for a few customers who haven't migrated to Aperture yet. "There are a lot of things the big providers don't do that blow my mind that the gateway does, like simply putting the cost in the response," he says. "We initially built those systems for customers ourselves, and the complexity is insane." Agents raised the s ## Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack DevFeed: [Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:56:11Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Nemotron](), [Nvidia](), [Artificial Intelligence](), [Vera Rubin](), [AI Infrastructure](), [AI Platform](), [cuOpt](), [Complex Systems](), [Optimization]() Tags: [ai](), [ai-infrastructure](), [artificial-intelligence](), [complex-systems](), [cuopt](), [enterprise](), [models](), [nemotron](), [nvidia](), [open](), [optimization](), [supply-chain](), [systems](), [vera-rubin]() ### AI overview Palantir and NVIDIA have deployed a sovereign AI stack for supply chain operations, initially using NVIDIA's own Vera Rubin supply chain as the first customer. The system combines Nemotron open models with Palantir Foundry and AIP, NVIDIA NeMo Data Libraries, and cuOpt to support materials allocation, scenario planning, optimization, and risk detection while keeping final decisions with supply chain experts. ### Source excerpt Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA's own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded The post Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack appeared first on StorageReview.com. ## Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots DevFeed: [Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-10T17:01:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [Multi-tenancy](), [AI Platform](), [GPU](), [benchmarking]() Tags: [ai](), [ai-operations](), [ai-platform](), [gpu](), [multi-tenancy](), [observability](), [platform-engineering]() ### AI overview Red Hat AI 3.5 adds multi-tenancy, isolation, priority-aware GPU scheduling, safety benchmarking, observability, and GPU resource management for enterprise AI workloads. ### Source excerpt Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with The post Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots appeared first on The New Stack. ## Nscale swallows lion's share of UK datacenter investment DevFeed: [Nscale swallows lion's share of UK datacenter investment]() Original publisher: [Read original article]() Author: Dan Robinson Published: 2026-09-10T14:08:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [datacenter](), [AI Platform]() Tags: [ai-infrastructure](), [datacenter](), [nscale](), [on-prem](), [united-kingdom]() ### AI overview Tracxn credits Nscale, an AI infrastructure company, with $3.7B of the UK datacenter sector's $5.7B investment haul. ### Source excerpt Tracxn credits one AI infrastructure outfit with $3.7B of sector's $5.7B haul ## d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment DevFeed: [d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment]() Original publisher: [Read original article]() Author: Jesse Clayton Published: 2026-09-10T13:00:21Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [NVLink](), [AI Platform](), [AI Inference](), [Deployment](), [networking](), [Latency](), [cpu](), [GPU]() Tags: [ai-inference](), [ai-infrastructure](), [ai-platform](), [corporate](), [d-matrix](), [deployment](), [gpu](), [latency](), [networking](), [nvidia](), [nvidia-vera](), [nvidia-vera-rubin](), [nvlink](), [spectrum-x](), [xpu]() ### AI overview d-Matrix announced plans to use NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs with NVIDIA AI infrastructure. The article describes using NVLink, Spectrum-X networking and MGX rack designs to support rack-scale, low-latency inference deployments. ### Source excerpt AI inference chipmaker d-Matrix today announced it will use NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA's AI infrastructure platform -- joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and Spectrum-X scale-out networking, the NVIDIA MGX rack architecture and the broader NVIDIA AI platform, NVLink Fusion gives [...] ## d-Matrix drinks the Nvidia Kool-Aid with NVLink Fusion and MGX rack designs DevFeed: [d-Matrix drinks the Nvidia Kool-Aid with NVLink Fusion and MGX rack designs]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-10T13:00:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [NVLink](), [d-matrix](), [AI Platform]() Tags: [ai](), [ai-infrastructure](), [amazon](), [d-matrix](), [datacenter](), [fujitsu](), [gpu](), [nvidia](), [nvlink](), [startup](), [systems](), [xpu]() ### AI overview d-Matrix, an AI infrastructure startup, is described as joining other companies as an NVLink supporter. The headline also references NVLink Fusion and MGX rack designs. ### Source excerpt AI infrastructure startup joins Qualcomm, Arm, Marvell, Amazon, Fujitsu, and MediaTek as NVLink true believers ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## CNCF Welcomes New Silver Members as Enterprises Scale AI From Training to Inference DevFeed: [CNCF Welcomes New Silver Members as Enterprises Scale AI From Training to Inference]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:58:47Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Cloud Native Ecosystem](), [Inference](), [AI Platform](), [Optimization](), [Kubernetes]() Tags: [ai](), [ai-infrastructure](), [announcements](), [cloud-native-ecosystem](), [inference](), [kubernetes](), [optimization](), [production](), [training]() ### AI overview CNCF announces nine new Silver members as enterprises shift AI workloads from training to production inference. The announcement emphasizes cloud-native infrastructure, operational efficiency, data sovereignty, and resource optimization. ### Source excerpt New members including SoftBank Corp. and Crusoe join the cloud native community to help build cost-efficient, sovereign infrastructure SHANGHAI, China - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 - September 8, 2026... ## 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 +... ## CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference DevFeed: [CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:50:55Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Cloud Native Ecosystem](), [Inference](), [AI Platform](), [model-deployment](), [distributed-systems](), [Back end]() Tags: [ai](), [ai-infrastructure](), [announcements](), [backend](), [china](), [cloud](), [cloud-native-ecosystem](), [developers](), [distributed-systems](), [inference](), [production](), [research]() ### AI overview CNCF and SlashData report that cloud native adoption in China is growing, including among IIoT and younger backend developers. The research describes cloud native infrastructure as supporting AI teams' transition from experimentation and training to production serving and distributed inference. ### Source excerpt New research finds China's IIoT developers (48%) outpace the global average (42%) in cloud native adoption as AI infrastructure matures Key Highlights: SHANGHAI - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China --Sept. 8,... ## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-04T17:54:45Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [AI Factory](), [AI Infrastructure](), [AI Platform](), [NVIDIA Vera Rubin](), [NVIDIA DGX](), [NVIDIA Vera](), [MLOps](), [NVIDIA DSX](), [ai-governance](), [Server]() Tags: [2026](), [ai](), [ai-factory](), [ai-governance](), [ai-infrastructure](), [asus](), [enterprise](), [mlops](), [nvidia-dsx](), [nvidia-vera](), [nvl72](), [object-storage](), [platform](), [storage](), [vera-rubin-nvl72]() ### AI overview ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware. ### Source excerpt ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer appeared first on StorageReview.com. ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## Announcing General Availability of VMware Cloud Foundation 9.1.1 DevFeed: [Announcing General Availability of VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T13:47:18Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [Cloud](), [Security](), [AI Platform](), [AI Models](), [MLOps]() Tags: [ai](), [ai-ml](), [ai-models](), [announce](), [apis](), [cloud](), [cloud-infrastructure](), [complexity](), [cost](), [cpu](), [data-privacy](), [evpn](), [governance](), [home-page](), [integrations](), [kubernetes](), [memory](), [nsx](), [private-ai-services](), [release](), [root-cause-analysis](), [security](), [troubleshooting](), [vcf-9-1](), [vcf-operations](), [vmware](), [vmware-cloud-foundation]() ### AI overview VMware announces the general availability of VMware Cloud Foundation 9.1.1. The release adds tougher security, vSAN Object Storage as a tech preview, multi-tenant AI model sharing with tenant-isolated access controls, and an AI Assistant for diagnostics, management-pack creation, and troubleshooting across infrastructure and Kubernetes clusters. ### Source excerpt Coming on the heels of a very successful VMware Explore in Vegas and the VMware Cloud Foundation (VCF) 9.1 launch in May, we're excited to announce the general availability of VCF 9.1.1. This release builds on VCF 9.1 with tougher security, vSAN Object Storage (tech preview, previously announced) and new capabilities designed to make your ... Continued The post Announcing General Availability of VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## 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,... ## Introducing v5 Droplets: next-generation performance, sized to your workload DevFeed: [Introducing v5 Droplets: next-generation performance, sized to your workload]() Original publisher: [Read original article]() Author: Krishna Nallamothu Published: 2026-08-26T02:19:47Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Hardware](), [cpu](), [AI Platform](), [web applications]() Tags: [agentic-ai](), [amd](), [audio](), [compute](), [digitalocean](), [droplets](), [performance](), [product-updates](), [v5](), [video](), [web-applications]() ### AI overview DigitalOcean announces the general availability of v5 Droplets, built on 5th Gen AMD EPYC processors. The release targets demanding workloads and offers independently configurable vCPU, memory, and storage, with up to 30% higher performance per core than previous-generation Droplets. ### Source excerpt We're excited to introduce v5 Droplets, a new generation of compute built on 5th Gen AMD EPYC™ processors. v5 Droplets are purpose built to deliver higher performance for demanding workloads such as compute-intensive agentic AI platforms, AI/ML tools, high throughput audio/video transcoding, and high-traffic distributed web applications and APIs. v5 Droplets deliver up to 30% higher performance per core than our previous-generation Droplets. For the first time, you can select vCPU, memory, and storage independently and pay for only the resources you choose. Your Droplet fits your application, and your bill reflects exactly what you used, nothing more. You can continue creating bundled Droplets the way you're used to, or choose v5 Droplets for next-generation workload-optimized performance. Designed for workloads that need more More teams are building AI applications, agent platforms, bursty data pipelines, and hosting high-traffic web applications on DigitalOcean than ever before, and those workloads demand higher performance. They need the faster cores, flexible memory ratios, and compute configurations that v5 Droplets provide. Starting with v5, every Droplet is tied to a hardware generation, so you get the same silicon and the same performance every time. You can choose Shared Droplets (s5) for bursty, variable work that doesn't need a full dedicated core, or General purpose Droplets (g5) for guaranteed, dedicated CPU, with memory ratios from 2x to 8x per vCPU. Pricing is simple and based on an hourly rate. You see each resource's price as you configure, a running total as you go, and one line per Droplet on your bill. Early customers running game servers and high throughput e-commerce applications saw 2x performance compared to their existing Droplets. No changes to existing Droplets pricing or experience Every existing Droplet plan stays exactly as it is and maintains the same prices, same bundles, same monthly caps, no migrations, and nothing new on your invoi ## Using deterministic systems and provenance to make AI-generated financial analysis verifiable DevFeed: [Using deterministic systems and provenance to make AI-generated financial analysis verifiable]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Trustworthy AI](), [Artificial Intelligence](), [AI Platform](), [Large Language Model](), [Code](), [Finance]() Tags: [ai](), [ai-platform](), [financial](), [models](), [provenance](), [trustworthy-ai](), [verification]() ### AI overview Kepler describes a model-agnostic approach to trustworthy AI that combines language models with deterministic tools for retrieval, computation, provenance, and traceability. The article focuses on making financial analysis outputs verifiable by linking numbers to their sources, formulas, or computations. ### Source excerpt Trustworthy AI takes more than a powerful model. See how Kepler uses deterministic systems and provenance to make financial analysis verifiable. ## How XPUs Meet a World-Class AI Factory DevFeed: [How XPUs Meet a World-Class AI Factory]() Original publisher: [Read original article]() Author: Jesse Clayton Published: 2026-08-24T15:00:54Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [AI Factory](), [NVLink](), [AI Infrastructure](), [Network](), [networking](), [Low Latency](), [moe](), [Artificial Intelligence](), [Nvidia](), [AI Platform](), [Latency]() Tags: [agentic-ai](), [ai-factory](), [ai-infrastructure](), [ecosystem](), [ethernet](), [gb300-nvl72](), [hardware](), [mixture-of-experts](), [networking](), [nvidia-dsx](), [nvl72](), [nvlink](), [software](), [time](), [xpu]() ### AI overview The article explains how NVLink Fusion combines custom XPUs with NVIDIA's established AI infrastructure to help build semi-custom AI factories. It focuses on scale-up networking, performance, resiliency, telemetry, platform maturity, and the economics of large-scale AI workloads. ### Source excerpt To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider [...] ## Kubeflow Has Graduated from CNCF DevFeed: [Kubeflow Has Graduated from CNCF]() Original publisher: [Read original article]() Author: Kubeflow Published: 2026-08-18T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [AI Infrastructure](), [AI Platform](), [Kubernetes](), [Cloud Native Ecosystem](), [distributed-training](), [Fine-tuning](), [model-serving]() Tags: [ai-infrastructure](), [announcement](), [cloud-native](), [cncf](), [distributed-training](), [fine-tuning](), [inference](), [kubernetes](), [model-serving](), [production]() ### AI overview Kubeflow has graduated from the Cloud Native Computing Foundation, recognizing its maturity and adoption as a Kubernetes-native platform for AI and machine learning workloads. The article highlights its community growth, enterprise use, security audit, governance work, and focus on scalable, portable infrastructure. ### Source excerpt Kubeflow is a CNCF Graduated Project ## Apollo Summit 2026: Turn Your API Platform Into Your AI Platform DevFeed: [Apollo Summit 2026: Turn Your API Platform Into Your AI Platform]() Original publisher: [Read original article]() Author: Eric Holzhauer Published: 2026-08-04T12:00:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [AI Platform](), [API Platform]() Tags: [2026](), [agent](), [agents](), [ai-agents](), [ai-platform](), [announcement](), [api-platform](), [apollo](), [graphos](), [graphql](), [mcp](), [summit]() ### AI overview Apollo Summit 2026 will take place in San Francisco from October 6 to 8. The article previews sessions and workshops on using governed GraphQL graphs as an access and context layer for applications and AI agents, including case studies from Block, Brex, Coolblue, and Starbucks. ### Source excerpt Turning an API platform into an AI platform starts with a governed graph. See real patterns from Block, Brex, and Starbucks at Apollo Summit 2026, Oct 6-8. [Next page]()