# Low Latency

Published articles for Low Latency.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin

DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin-26913.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

Published: 2026-09-15T16:55:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Groq 3 LPX](<https://devfeed.tech/topics/groq-3-lpx.md>), [LPX](<https://devfeed.tech/topics/lpx.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [groq](<https://devfeed.tech/topics/groq.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [drive](<https://devfeed.tech/tags/drive.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [power-management](<https://devfeed.tech/tags/power-management.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis.

### Source excerpt

Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...

## How to operate shared platforms safely at agent scale

DevFeed: [How to operate shared platforms safely at agent scale](<https://devfeed.tech/articles/how-to-operate-shared-platforms-safely-at-agent-scale-26970.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/operating-shared-platforms-agent-scale/>)

Author: Candace Shamieh; T Zhang; Gabriele Baldoni

Published: 2026-09-15T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [Platform Engineering](<https://devfeed.tech/topics/platform-engineering.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [sdlc](<https://devfeed.tech/topics/sdlc.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Software](<https://devfeed.tech/topics/software.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-observability](<https://devfeed.tech/tags/agent-observability.md>), [agentic-workflows](<https://devfeed.tech/tags/agentic-workflows.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ci](<https://devfeed.tech/tags/ci.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [operational](<https://devfeed.tech/tags/operational.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [platforms](<https://devfeed.tech/tags/platforms.md>), [queue](<https://devfeed.tech/tags/queue.md>), [recovery](<https://devfeed.tech/tags/recovery.md>), [timeout](<https://devfeed.tech/tags/timeout.md>)

### AI overview

This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries.

### Source excerpt

Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms.

## 🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra

DevFeed: [🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra](<https://devfeed.tech/articles/pinterest-s-fix-for-the-hardest-problem-in-ml-infra-18131.md>)

Original publisher: [Read original article](<https://hungrymindsdev.substack.com/p/pinterests-fix-for-the-hardest-problem>)

Author: Alexandre Zajac

Published: 2026-09-14T15:31:30Z

Content type: article

Language: en

Sources: [Hungry Minds](<https://devfeed.tech/sources/hungry-minds.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [data](<https://devfeed.tech/topics/data.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>)

Tags: [batch](<https://devfeed.tech/tags/batch.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [data](<https://devfeed.tech/tags/data.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [ml](<https://devfeed.tech/tags/ml.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### AI overview

Pinterest redesigned its user-sequence platform for ranking, retrieval, and recommendation systems by defining signals once and instantiating them consistently across streaming, batch, and serving workloads. The approach uses Python configuration with validated schemas, a shared execution engine, cooperating streaming and batch paths, and columnar time-partitioned storage to improve freshness, completeness, consistency, and operational efficiency.

### Source excerpt

PLUS: OpenAI agents beat math 🧮, Test techniques for agents ⚡, Postgres survival guide 📖

## Infineon RISC-V for Automotive at Hot Chips 2026

DevFeed: [Infineon RISC-V for Automotive at Hot Chips 2026](<https://devfeed.tech/articles/infineon-risc-v-for-automotive-at-hot-chips-2026-14009.md>)

Original publisher: [Read original article](<https://www.servethehome.com/infineon-risc-v-for-automotive-at-hot-chips-2026/>)

Author: Vic A

Published: 2026-09-13T21:58:44Z

Content type: article

Language: en

Sources: [ServeTheHome](<https://devfeed.tech/sources/servethehome.md>)

Topics: [RISC-V](<https://devfeed.tech/topics/riscv.md>), [Microcontroller](<https://devfeed.tech/topics/microcontroller.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Security](<https://devfeed.tech/topics/security.md>), [IO](<https://devfeed.tech/topics/io.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [automotive](<https://devfeed.tech/tags/automotive.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [infineon](<https://devfeed.tech/tags/infineon.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [microcontrollers](<https://devfeed.tech/tags/microcontrollers.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [performance](<https://devfeed.tech/tags/performance.md>), [posix](<https://devfeed.tech/tags/posix.md>), [processors](<https://devfeed.tech/tags/processors.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [risc-v](<https://devfeed.tech/tags/risc-v.md>), [security](<https://devfeed.tech/tags/security.md>)

### 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.

## Chip Huyen explains how to cut inference costs without new hardware

DevFeed: [Chip Huyen explains how to cut inference costs without new hardware](<https://devfeed.tech/articles/chip-huyen-explains-how-to-cut-inference-costs-without-new-hardware-10830.md>)

Original publisher: [Read original article](<https://thenewstack.io/pg-99-conf-2026-inference-costs/>)

Author: Tim Koopmans

Published: 2026-09-13T15:00:00Z

Content type: article

Language: en

Sources: [The New Stack](<https://devfeed.tech/sources/the-new-stack.md>)

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Frontier Model](<https://devfeed.tech/topics/frontier-model.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [post-contributed](<https://devfeed.tech/tags/post-contributed.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [sponsor-scylladb](<https://devfeed.tech/tags/sponsor-scylladb.md>), [sponsored](<https://devfeed.tech/tags/sponsored.md>), [sponsored-post-contributed](<https://devfeed.tech/tags/sponsored-post-contributed.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

Chip Huyen explains why inference costs can outweigh one-time frontier-model training costs and outlines ways to optimize inference without new hardware. The article emphasizes latency metrics such as time to first token, time per output token, end-to-end latency, and goodput, especially for reasoning models.

### Source excerpt

Last October, the P99 conference -- the online gathering for developers focused on high-performance, low-latency applications -- featured a cracking The post Chip Huyen explains how to cut inference costs without new hardware appeared first on The New Stack.

## 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](<https://devfeed.tech/articles/second-gen-single-rack-aws-outposts-puts-2-688-vcpus-and-100tb-of-ebs-in-one-42u-rack-12378.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/second-gen-single-rack-aws-outposts-puts-2688-vcpus-and-100tb-of-ebs-in-one-42u-rack>)

Author: Harold Fritts

Published: 2026-09-12T18:24:22Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [AWS Outposts](<https://devfeed.tech/topics/aws-outposts.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Network](<https://devfeed.tech/topics/network.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Security](<https://devfeed.tech/topics/security.md>)

Tags: [5g](<https://devfeed.tech/tags/5g.md>), [automation](<https://devfeed.tech/tags/automation.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-outposts](<https://devfeed.tech/tags/aws-outposts.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [compute](<https://devfeed.tech/tags/compute.md>), [connectx](<https://devfeed.tech/tags/connectx.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [governance](<https://devfeed.tech/tags/governance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [networking](<https://devfeed.tech/tags/networking.md>), [security](<https://devfeed.tech/tags/security.md>)

### 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.

## Patches Ready For AMDGPU HDMI 2.1 Enabled By Default With Linux 7.4 With FreeSync, VRR & ALLM

DevFeed: [Patches Ready For AMDGPU HDMI 2.1 Enabled By Default With Linux 7.4 With FreeSync, VRR & ALLM](<https://devfeed.tech/articles/patches-ready-for-amdgpu-hdmi-2-1-enabled-by-default-with-linux-7-4-with-freesync-vrr-allm-12416.md>)

Original publisher: [Read original article](<https://www.phoronix.com/news/Linux-7.4-AMDGPU-HDMI-2.1-Go>)

Author: Michael Larabel

Published: 2026-09-11T10:39:00Z

Content type: news

Language: en

Sources: [Phoronix](<https://devfeed.tech/sources/phoronix.md>)

Topics: [hdmi](<https://devfeed.tech/topics/hdmi.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [desktop-linux](<https://devfeed.tech/tags/desktop-linux.md>), [driver](<https://devfeed.tech/tags/driver.md>), [gaming](<https://devfeed.tech/tags/gaming.md>), [graphics](<https://devfeed.tech/tags/graphics.md>), [hdmi](<https://devfeed.tech/tags/hdmi.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [linux-benchmarking](<https://devfeed.tech/tags/linux-benchmarking.md>), [linux-hardware-benchmarks](<https://devfeed.tech/tags/linux-hardware-benchmarks.md>), [linux-hardware-reviews](<https://devfeed.tech/tags/linux-hardware-reviews.md>), [linux-how-to](<https://devfeed.tech/tags/linux-how-to.md>), [linux-performance](<https://devfeed.tech/tags/linux-performance.md>), [linux-server-benchmarks](<https://devfeed.tech/tags/linux-server-benchmarks.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source-graphics](<https://devfeed.tech/tags/open-source-graphics.md>), [phoronix](<https://devfeed.tech/tags/phoronix.md>), [phoronix-test-suite](<https://devfeed.tech/tags/phoronix-test-suite.md>), [release](<https://devfeed.tech/tags/release.md>), [ubuntu-benchmarks](<https://devfeed.tech/tags/ubuntu-benchmarks.md>), [ubuntu-hardware](<https://devfeed.tech/tags/ubuntu-hardware.md>), [updates](<https://devfeed.tech/tags/updates.md>)

### AI overview

The article reports that most AMDGPU HDMI 2.1 support is expected to merge for Linux 7.4 and be enabled by default. The work includes FreeSync, VRR, ALLM, and HDMI FRL support, along with updates to AMD graphics and display engines.

### Source excerpt

Linux 7.4 is now set to reach the elusive milestone of HDMI 2.1 support for the AMDGPU kernel graphics driver. After the HDMI Forum previously rejected HDMI 2.1 for the AMDGPU open-source driver implementation going back years, earlier this year something changed -- widely speculated to be with Valve's involvement -- that HDMI 2.1 patches began appearing for the AMDGPU driver. With the upcoming Linux 7.4 cycle, the bulk of that work will now be in place and enabled by default...

## Improving Lakebase Postgres compute cache

DevFeed: [Improving Lakebase Postgres compute cache](<https://devfeed.tech/articles/improving-lakebase-postgres-compute-cache-11541.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/improving-lakebase-postgres-compute-cache>)

Author: David Wein; Sunil Kamath; Haoyu Huang

Published: 2026-09-10T13:47:03Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Filesystems](<https://devfeed.tech/topics/filesystems.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>)

Tags: [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [cache](<https://devfeed.tech/tags/cache.md>), [caching](<https://devfeed.tech/tags/caching.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [filesystem](<https://devfeed.tech/tags/filesystem.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [s3](<https://devfeed.tech/tags/s3.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances.

### Source excerpt

The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible...

## Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency

DevFeed: [Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency](<https://devfeed.tech/articles/adaptive-instructed-retriever-frontier-quality-search-at-2x-lower-latency-11536.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/adaptive-instructed-retriever-frontier-quality-search-2x-lower-latency>)

Author: Cindy Wang; Cheng Li; Jialu Liu; Sean Kulinski; Arnav Singhvi; Wen Sun; Michael Bendersky

Published: 2026-09-09T13:30:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [speed](<https://devfeed.tech/tags/speed.md>), [third-party](<https://devfeed.tech/tags/third-party.md>)

### AI overview

Databricks introduces Adaptive Instructed-Retriever, a retrieval model that combines fast parallel search with sequential multi-step search for harder enterprise queries. It adaptively spends extra computation only when useful, achieving comparable quality to leading third-party models at twice lower latency while improving over single-step retrieval on reported benchmarks.

### Source excerpt

Effective enterprise data agents require search that is both accurate and fast. Earlier...

## Video Streaming Technology: Protocols, Codecs, CDNs, and 2026 Trends

DevFeed: [Video Streaming Technology: Protocols, Codecs, CDNs, and 2026 Trends](<https://devfeed.tech/articles/the-definitive-guide-to-video-streaming-technology-in-2026-38030.md>)

Original publisher: [Read original article](<https://www.dacast.com/blog/video-streaming-technology/>)

Author: Jon Whitehead

Published: 2026-09-08T01:00:42Z

Content type: tutorial

Language: en

Sources: [DaCast](<https://devfeed.tech/sources/dacast.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [5G](<https://devfeed.tech/topics/5g.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [WebRTC](<https://devfeed.tech/topics/webrtc.md>), [Virtual reality](<https://devfeed.tech/topics/virtual-reality.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [ai](<https://devfeed.tech/tags/ai.md>), [cdn](<https://devfeed.tech/tags/cdn.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [guide](<https://devfeed.tech/tags/guide.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [technology](<https://devfeed.tech/tags/technology.md>), [the-video-experts-blog](<https://devfeed.tech/tags/the-video-experts-blog.md>), [video](<https://devfeed.tech/tags/video.md>), [video-streaming](<https://devfeed.tech/tags/video-streaming.md>)

### AI overview

This guide explains how video streaming works, covering data encoding, delivery through content delivery networks, decoding by video players, streaming protocols, codecs, and security. It also discusses AI-driven tools, low-latency streaming, 5G, edge computing, virtual reality, and business applications in 2026.

### Source excerpt

By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Video streaming technology has revolutionized the way businesses and individuals share and consume content. Whether it's live streaming a product launch, hosting virtual events, delivering training sessions, or providing entertainment through on-demand video services, streaming technology is now an [...] The post The Definitive Guide to Video Streaming Technology in 2026 appeared first on Dacast.

## Choosing a low-latency infrastructure layer for conversational AI

DevFeed: [Choosing a low-latency infrastructure layer for conversational AI](<https://devfeed.tech/articles/choosing-a-low-latency-infrastructure-layer-for-conversational-ai-16112.md>)

Original publisher: [Read original article](<https://www.twilio.com/en-us/blog/insights/low-latency-layer-conversational-ai>)

Author: Luke Morgan

Published: 2026-09-08T00:00:00Z

Content type: article

Language: en

Sources: [Twilio Blog](<https://devfeed.tech/sources/twilio-blog.md>)

Topics: [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Conversational AI](<https://devfeed.tech/topics/conversational-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [industry-insights](<https://devfeed.tech/tags/industry-insights.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [voice-ai](<https://devfeed.tech/tags/voice-ai.md>)

### AI overview

This article explains how infrastructure choices affect latency in conversational AI call-center systems. It describes a unified path for speech recognition, language-model processing, and speech synthesis, and presents Twilio's ConversationRelay as a low-latency voice pipeline with median latency under 0.5 seconds.

### Source excerpt

ConversationRelay delivers real-time speech recognition for call centers, with under 0.5s median latency. See how Twilio powers low-latency conversational AI.

## 🍔🧠 How OpenAI Built GPT-Live for Low-Latency Voice AI

DevFeed: [🍔🧠 How OpenAI Built GPT-Live for Low-Latency Voice AI](<https://devfeed.tech/articles/how-openai-built-gpt-live-for-low-latency-voice-ai-18127.md>)

Original publisher: [Read original article](<https://hungrymindsdev.substack.com/p/how-openai-built-gpt-live-for-low>)

Author: Alexandre Zajac

Published: 2026-09-07T15:31:33Z

Content type: article

Language: en

Sources: [Hungry Minds](<https://devfeed.tech/sources/hungry-minds.md>)

Topics: [Conversational AI](<https://devfeed.tech/topics/conversational-ai.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [voice ai](<https://devfeed.tech/topics/voice-ai.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Remote Procedure Call (RPC)](<https://devfeed.tech/topics/rpc.md>), [Go](<https://devfeed.tech/topics/go.md>)

Tags: [go](<https://devfeed.tech/tags/go.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [openai](<https://devfeed.tech/tags/openai.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [rpc](<https://devfeed.tech/tags/rpc.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [voice-ai](<https://devfeed.tech/tags/voice-ai.md>)

### AI overview

The article explains how OpenAI built GPT-Live as a low-latency, full-duplex voice system. It describes a fast audio path that listens and speaks simultaneously, asynchronous delegation of deeper reasoning and backend work, stateful handoffs, optimized protocol boundaries, and a Go-based media frontend.

### Source excerpt

PLUS: Zero-knowledge proofs ⚡, Design.md agent automation 👨💻, System Design Docs 101 📚

## Build a real-time market data app with ClickHouse and Massive

DevFeed: [Build a real-time market data app with ClickHouse and Massive](<https://devfeed.tech/articles/build-a-real-time-market-data-app-with-clickhouse-and-massive-5000.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/build-a-real-time-market-data-app-with-clickhouse-and-polygonio>)

Author: Lionel Palacin

Published: 2026-09-04T00:00:00Z

Content type: tutorial

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [App](<https://devfeed.tech/topics/app.md>), [data](<https://devfeed.tech/topics/data.md>), [Node.js](<https://devfeed.tech/topics/node-js.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [backend](<https://devfeed.tech/tags/backend.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [compression](<https://devfeed.tech/tags/compression.md>), [data](<https://devfeed.tech/tags/data.md>), [demo](<https://devfeed.tech/tags/demo.md>), [events](<https://devfeed.tech/tags/events.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [node](<https://devfeed.tech/tags/node.md>), [node-js](<https://devfeed.tech/tags/node-js.md>), [react](<https://devfeed.tech/tags/react.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [rest-api](<https://devfeed.tech/tags/rest-api.md>), [storage](<https://devfeed.tech/tags/storage.md>), [streams](<https://devfeed.tech/tags/streams.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

A tutorial for building a real-time market-data application that ingests, stores, and queries trade and quote ticks with Massive and ClickHouse, using Node.js for backend work and React for live visualization.

### Source excerpt

Learn how to build a real-time financial analytics application with Massive and ClickHouse that scales to thousands of events per second.

## VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University

DevFeed: [VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University](<https://devfeed.tech/articles/vdura-deploys-high-performance-storage-platform-for-ai-and-hpc-at-new-mexico-state-university-12380.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/vdura-deploys-high-performance-storage-platform-for-ai-and-hpc-at-new-mexico-state-university>)

Author: Harold Fritts

Published: 2026-09-02T10:00:00Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>), [Data Infrastructure](<https://devfeed.tech/topics/data-infrastructure.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [Post-quantum cryptography](<https://devfeed.tech/topics/post-quantum-cryptography.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [enterprise-storage](<https://devfeed.tech/tags/enterprise-storage.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [post-quantum-cryptography-pqc](<https://devfeed.tech/tags/post-quantum-cryptography-pqc.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

VDURA has moved its storage platform at New Mexico State University into full production to support AI and high-performance computing research. The deployment combines NVMe flash and high-density HDD tiers through a global namespace over InfiniBand, allowing separate scaling of performance and capacity. It also supports NMSU's post-quantum cryptography research and large-scale data pipeline projects.

### Source excerpt

VDURA has completed the deployment of its data platform at New Mexico State University (NMSU), moving the system into full production. The infrastructure is designed to serve the university's research community with a high-durability, high-throughput storage environment tailored specifically for artificial intelligence and high-performance computing (HPC) workloads. NMSU, which holds Carnegie R1 status and manages The post VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University appeared first on StorageReview.com.

## Video Streaming Protocols: 6 Preferred Formats for Professional Broadcasting

DevFeed: [Video Streaming Protocols: 6 Preferred Formats for Professional Broadcasting](<https://devfeed.tech/articles/video-streaming-protocols-6-preferred-formats-for-professional-broadcasting-38028.md>)

Original publisher: [Read original article](<https://www.dacast.com/blog/video-streaming-protocol/>)

Author: Jon Whitehead

Published: 2026-09-02T09:35:50Z

Content type: tutorial

Language: en

Sources: [DaCast](<https://devfeed.tech/sources/dacast.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [Protocol (disambiguation)](<https://devfeed.tech/topics/protocol.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [WebRTC](<https://devfeed.tech/topics/webrtc.md>), [Playback](<https://devfeed.tech/topics/playback.md>), [format](<https://devfeed.tech/topics/format.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [format](<https://devfeed.tech/tags/format.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [playback](<https://devfeed.tech/tags/playback.md>), [protocols](<https://devfeed.tech/tags/protocols.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [the-video-experts-blog](<https://devfeed.tech/tags/the-video-experts-blog.md>), [video](<https://devfeed.tech/tags/video.md>), [video-streaming](<https://devfeed.tech/tags/video-streaming.md>), [webrtc](<https://devfeed.tech/tags/webrtc.md>)

### AI overview

This guide explains six video streaming protocols used in professional broadcasting: HLS, RTMP, WebRTC, SRT, RTSP, and MPEG-DASH. It compares their purposes, strengths, limitations, latency, compatibility, and suitable use cases. It presents HLS as a broadly compatible delivery default, RTMP as an ingest standard, WebRTC for real-time two-way interaction, SRT for one-way broadcasting over unreliable networks, RTSP for surveillance and IoT, and MPEG-DASH as an open alternative with limited Apple support.

### Source excerpt

By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 A video streaming protocol is the set of rules that governs how video data moves from your encoder to your streaming host to the video player your audience watches on. Common examples include RTMP, HLS, WebRTC, and SRT, each [...] The post Video Streaming Protocols: 6 Preferred Formats for Professional Broadcasting appeared first on Dacast.

## A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres

DevFeed: [A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres](<https://devfeed.tech/articles/a-universal-interface-how-quintoandar-made-clickhouse-plug-and-play-with-managed-postgres-5540.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/quintoandar-managed-postgres>)

Author: ClickHouse

Published: 2026-08-27T00:00:00Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [data](<https://devfeed.tech/topics/data.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [house](<https://devfeed.tech/tags/house.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [tech-lead](<https://devfeed.tech/tags/tech-lead.md>)

### AI overview

QuintoAndar rebuilt its customer data platform on ClickHouse Cloud, consolidating roughly 900 million monthly events from 14 million users and serving about 300 million API requests. The team replaced separate low-latency and batch-processing pipelines by writing events directly to ClickHouse. ClickHouse Managed Postgres provided compatibility with Hightouch and other Postgres-compatible tools.

### Source excerpt

QuintoAndar unified ~900 million monthly events in ClickHouse Cloud and used ClickHouse Managed Postgres to make that data accessible to Hightouch and any Postgres-compatible tool.

## AI, IPv6, and the future Internet at IETF 126

DevFeed: [AI, IPv6, and the future Internet at IETF 126](<https://devfeed.tech/articles/ai-ipv6-and-the-future-internet-at-ietf-126-10846.md>)

Original publisher: [Read original article](<https://blog.apnic.net/2026/08/25/ai-ipv6-and-the-future-internet-at-ietf-126/>)

Author: Anlei Hu

Published: 2026-08-25T01:20:50Z

Content type: article

Language: en

Sources: [APNIC Blog](<https://devfeed.tech/sources/apnic-blog.md>)

Topics: [Internet](<https://devfeed.tech/topics/internet.md>), [Internet Engineering Task Force (IETF)](<https://devfeed.tech/topics/ietf.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Network](<https://devfeed.tech/topics/network.md>), [Security](<https://devfeed.tech/topics/security.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [dns](<https://devfeed.tech/tags/dns.md>), [guest-post](<https://devfeed.tech/tags/guest-post.md>), [ietf](<https://devfeed.tech/tags/ietf.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [internet](<https://devfeed.tech/tags/internet.md>), [internet-infrastructure](<https://devfeed.tech/tags/internet-infrastructure.md>), [ipv6](<https://devfeed.tech/tags/ipv6.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [nat](<https://devfeed.tech/tags/nat.md>), [network](<https://devfeed.tech/tags/network.md>), [networks](<https://devfeed.tech/tags/networks.md>), [opinion](<https://devfeed.tech/tags/opinion.md>), [security](<https://devfeed.tech/tags/security.md>), [standards](<https://devfeed.tech/tags/standards.md>), [tech-matters](<https://devfeed.tech/tags/tech-matters.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### AI overview

The article reports on IETF 126 discussions about how AI is shaping Internet infrastructure. It presents IPv6 as important for the scale, globally unique addressing, and traceability required by distributed AI deployments and agents, while DNS remains central to service discovery. It also covers IPv4/IPv6 mapping, NAT-related identity and security concerns, SRv6, low-latency forwarding, IOAM telemetry, and proposals for DNS-based AI agent discovery.

### Source excerpt

Guest Post: At IETF 126, discussions highlighted how AI is beginning to shape the future of Internet infrastructure. Participants identified IPv6 as a critical foundation for large-scale AI ecosystems and explored new approaches.

## Golden Paths for AI agents: What changes when platform users aren't human?

DevFeed: [Golden Paths for AI agents: What changes when platform users aren't human?](<https://devfeed.tech/articles/golden-paths-for-ai-agents-what-changes-when-platform-users-aren-t-human-2277.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/golden-paths-for-ai-agents/>)

Author: Candace Shamieh; Shlomo Benyaminov; James Eastham

Published: 2026-08-25T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [API](<https://devfeed.tech/topics/api.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [agent-observability](<https://devfeed.tech/tags/agent-observability.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-security](<https://devfeed.tech/tags/ai-security.md>), [api](<https://devfeed.tech/tags/api.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [developers](<https://devfeed.tech/tags/developers.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [platform](<https://devfeed.tech/tags/platform.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Golden Paths for AI agents must evolve beyond human-oriented development workflows. The article explains how platform teams can design agent-facing paths around workload requirements, machine-consumable and enforceable platform capabilities, execution patterns, and controlled workflow dispatch.

### Source excerpt

Golden Paths for AI agents require intentional execution patterns, machine-consumable contracts, and dispatch controls. Here's how to build them.

## MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet

DevFeed: [MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet](<https://devfeed.tech/articles/metaroce-a-new-rdma-transport-built-for-ai-scale-ethernet-130.md>)

Original publisher: [Read original article](<https://engineering.fb.com/2026/08/24/networking-traffic/metaroce-rdma-transport-ai-ethernet/>)

Author: Arvind Srinivasan; Neil Spring; Omar Baldonado; Rajiv Krishnamurthy

Published: 2026-08-24T18:02:29Z

Content type: article

Language: en

Sources: [Engineering at Meta](<https://devfeed.tech/sources/engineering-at-meta.md>)

Topics: [Ethernet](<https://devfeed.tech/topics/ethernet.md>), [Networks](<https://devfeed.tech/topics/networks.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [data centers](<https://devfeed.tech/topics/data-centers.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [data-center-engineering](<https://devfeed.tech/tags/data-center-engineering.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [ethernet](<https://devfeed.tech/tags/ethernet.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [networking-traffic](<https://devfeed.tech/tags/networking-traffic.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### AI overview

Meta introduces MetaRoCE, an RDMA transport protocol designed for AI workloads on commodity Ethernet at million-GPU scale. The article describes its release through the Open Compute Project and explains how endpoint intelligence, packet spraying, fine-grained logical paths, and real-time telemetry aim to provide high throughput, low tail latency, and operational simplicity for distributed training and inference.

### Source excerpt

Training and serving frontier AI models depends on fast, reliable networks that move data between GPUs without wasting compute cycles. To meet this challenge at scale, Meta designed MetaRoCE - a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet. We're releasing the MetaRoCE specification, a reference software implementation and a compliance test [...] Read More... The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineering at Meta.

## Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules

DevFeed: [Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules](<https://devfeed.tech/articles/giga-scale-ai-and-the-ethernet-evolution-how-spectrum-x-ethernet-rewrites-the-rules-6830.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/giga-scale-ai-ethernet-evolution-spectrum-x-ethernet-rewrites-rules/>)

Author: Elizabeth Goodman

Published: 2026-08-24T15:08:39Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Spectrum-X](<https://devfeed.tech/topics/spectrum-x.md>), [Ethernet](<https://devfeed.tech/topics/ethernet.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [networking](<https://devfeed.tech/topics/networking.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-networking](<https://devfeed.tech/tags/ai-networking.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [ethernet](<https://devfeed.tech/tags/ethernet.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [internet-communications](<https://devfeed.tech/tags/internet-communications.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [spectrum-x](<https://devfeed.tech/tags/spectrum-x.md>)

### AI overview

This article explains how the growth of distributed generative AI training has made scale-out networking a major data center performance bottleneck. It contrasts traditional Ethernet with NVIDIA Spectrum-X Ethernet, a hardware-accelerated architecture that co-designs switches and host-side NICs to provide predictable low latency, high fabric utilization, and resilience for large AI workloads. It also introduces Spectrum-X Multiplane technology and describes how AI collective communication exposes limitations in conventional ECMP routing and congestion handling.

### Source excerpt

The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs,...

## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency

DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency](<https://devfeed.tech/articles/how-sprig-replaced-postgres-clickhouse-redis-with-4-8x-better-latency-4880.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/24/sprig-replaced-postgres-clickhouse-redis-4-8x-better-latency/>)

Author: Cynthia Dunlop

Published: 2026-08-24T13:30:56Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [backend](<https://devfeed.tech/tags/backend.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [data](<https://devfeed.tech/tags/data.md>), [databases](<https://devfeed.tech/tags/databases.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [redis](<https://devfeed.tech/tags/redis.md>), [user-stories](<https://devfeed.tech/tags/user-stories.md>)

### AI overview

Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis.

### Source excerpt

With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases.

## Do IXPs make the Internet faster? It's complicated

DevFeed: [Do IXPs make the Internet faster? It's complicated](<https://devfeed.tech/articles/do-ixps-make-the-internet-faster-it-s-complicated-10844.md>)

Original publisher: [Read original article](<https://blog.apnic.net/2026/08/24/do-ixps-make-the-internet-faster-its-complicated/>)

Author: Dan Fidler

Published: 2026-08-23T23:05:33Z

Content type: article

Language: en

Sources: [APNIC Blog](<https://devfeed.tech/sources/apnic-blog.md>)

Topics: [Internet](<https://devfeed.tech/topics/internet.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Network](<https://devfeed.tech/topics/network.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [australia](<https://devfeed.tech/tags/australia.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [content](<https://devfeed.tech/tags/content.md>), [cost](<https://devfeed.tech/tags/cost.md>), [events](<https://devfeed.tech/tags/events.md>), [global](<https://devfeed.tech/tags/global.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [internet](<https://devfeed.tech/tags/internet.md>), [ixps](<https://devfeed.tech/tags/ixps.md>), [latency](<https://devfeed.tech/tags/latency.md>), [local](<https://devfeed.tech/tags/local.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [netflix](<https://devfeed.tech/tags/netflix.md>), [network](<https://devfeed.tech/tags/network.md>), [networks](<https://devfeed.tech/tags/networks.md>), [performance](<https://devfeed.tech/tags/performance.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [routing](<https://devfeed.tech/tags/routing.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [tech-matters](<https://devfeed.tech/tags/tech-matters.md>), [technology](<https://devfeed.tech/tags/technology.md>), [thailand](<https://devfeed.tech/tags/thailand.md>), [thainog](<https://devfeed.tech/tags/thainog.md>)

### AI overview

IXPs do not make the Internet uniformly faster. Their impact depends on how performance is defined, where content is hosted or cached, the type of exchange, and the surrounding network conditions. The article examines latency, throughput, streaming quality, reliability, routing, and the changing meaning of keeping traffic local.

### Source excerpt

IXPs shape latency, cost, resilience, and the spread of new technologies, not just Internet speed. As the Internet evolves, the role of IXPs continues to expand, as highlighted in this BPF 2026 panel discussion.

## Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks

DevFeed: [Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks](<https://devfeed.tech/articles/quantum-augmented-applications-integrating-quantum-subroutines-into-classical-software-stacks-2209.md>)

Original publisher: [Read original article](<https://stackoverflow.blog/2026/08/20/quantum-augmented-applications-integrating-quantum-subroutines-into-classical-software-stacks/>)

Author: Dr. Ahmad Mateen Ishanzai

Published: 2026-08-20T18:43:39Z

Content type: article

Language: en

Sources: [Stack Overflow Blog](<https://devfeed.tech/sources/stack-overflow-blog.md>)

Topics: [Software](<https://devfeed.tech/topics/software.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [coding](<https://devfeed.tech/topics/coding.md>), [Network](<https://devfeed.tech/topics/network.md>)

Tags: [buiilding-software](<https://devfeed.tech/tags/buiilding-software.md>), [cc-by-sa](<https://devfeed.tech/tags/cc-by-sa.md>), [combinatorial-optimization](<https://devfeed.tech/tags/combinatorial-optimization.md>), [contributed](<https://devfeed.tech/tags/contributed.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>)

### AI overview

This article presents quantum-augmented applications as hybrid systems that integrate Quantum Processing Units with classical software pipelines. It describes delegating targeted computationally difficult subroutines to QPUs while retaining classical business logic, data preprocessing, orchestration, and optimization, and discusses practical constraints including noise, transpilation latency, and network overhead.

### Source excerpt

Founded in 2008, Stack Overflow's public platform is used by nearly everyone who codes to learn, share their knowledge, collaborate, and build their careers.

## NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation

DevFeed: [NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation](<https://devfeed.tech/articles/nvidia-jetpack-7-2-1-adds-agentic-video-skills-and-t3000-emulation-6897.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-jetpack-7-2-1-adds-agentic-video-skills-and-t3000-emulation/>)

Author: Elizabeth Goodman

Published: 2026-08-11T19:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Jetson](<https://devfeed.tech/topics/jetson.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [Python](<https://devfeed.tech/topics/python.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Automation](<https://devfeed.tech/topics/automation.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [automation](<https://devfeed.tech/tags/automation.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [jetpack](<https://devfeed.tech/tags/jetpack.md>), [jetson](<https://devfeed.tech/tags/jetson.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [python](<https://devfeed.tech/tags/python.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [robotics-compute](<https://devfeed.tech/tags/robotics-compute.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [video-analytics](<https://devfeed.tech/tags/video-analytics.md>), [video-codec-sdk](<https://devfeed.tech/tags/video-codec-sdk.md>)

### AI overview

NVIDIA JetPack 7.2.1 adds PyNvVideoCodec 2.2 support on Jetson Thor, enabling Python-based hardware video encoding and decoding with GPU-resident frames. It also introduces agentic video skills that turn developer goals into device inspection, configuration, execution, measurement, and evidence-driven codec workflows.

### Source excerpt

Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media...

[Next page](<https://devfeed.tech/tags/low-latency.md?cursor=WyIyMDI2LTA4LTExVDE5OjAwOjAwKzAwOjAwIiwgIjRmNjMyMTRlLTA3N2ItNDE3ZS04N2M5LThjNDRmZWU5NmVlMiJd>)