# AI Inference The process of applying a trained AI model to input data to generate predictions or responses. 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. ## CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) DevFeed: [CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding)]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-16T00:00:55Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Artificial Intelligence](), [Embedded Systems](), [AI Inference](), [V](), [Arm](), [cpu](), [Open Source](), [Ethernet](), [Linux](), [Toit]() Tags: [ai](), [arm](), [artificial-intelligence-ai](), [camera](), [computer-vision](), [cpu](), [debug](), [edge-ai](), [embedded](), [ethernet](), [hardware](), [inference](), [kickstarter](), [linux](), [module](), [open-source](), [real-time](), [risc-v](), [robotics](), [rt-thread](), [soc](), [sophgo](), [tinyml](), [usb](), [video](), [vision]() ### AI overview The AIMORELOGY Ovis is an open-source modular AI vision camera built around the CVITEK CV1842H-P SoC. It provides full-color 1080p night vision, 1.5 TOPS edge AI inference, USB and Ethernet connectivity, and a dual-OS environment using Linux and RT-Thread. ### Source excerpt The AIMORELOGY Ovis is an open-source AI vision camera module built around the CVITEK CV1842H-P SoC, with full-color 1080p night vision and 1.5 TOPS edge AI inference in a compact modular design. It is designed for drones, robotics, security systems, smart cameras, and custom embedded vision products. The camera features a compact stacked design, with a 20 x 20 mm Core board that includes the CVITEK CV1842H-P SoC, 2 Gbit NAND flash, USB, and UART debug pads. A Sensor board with the SC235HAI image sensor connects on top and also adds Ethernet and UART interfaces. An optional CVBS board goes between the Sensor board and the Ovis Core board. AIMORELOGY Ovis specifications: Ovis Core Board SoC - CVITEK CV1842H-P CPU - 1x Arm Cortex-A53 core @ 1.1 GHz, 1x RISC-V C906 core @ 800 MHz NPU - 1.5 TOPS @ INT8 with BF16 support ISP - AI-ISP with real-time 1080p [...] The post CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November DevFeed: [Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-14T18:03:44Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [AI Infrastructure](), [Inference Performance](), [cpu](), [data centers](), [Confidential Computing](), [Arm](), [Hardware](), [NVLink](), [Nvidia]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [arm](), [benchmark](), [data-centers](), [enterprise](), [fujitsu](), [hardware](), [nvidia](), [nvlink]() ### AI overview Fujitsu is introducing MONAKA Servers built around its 2nm FUJITSU-MONAKA processor for AI inference in air-cooled data centers. The servers offer up to 144 CPU cores, matrix instructions, SVE2 vector processing, hardware-level confidential computing, and planned NVLink Fusion integration with NVIDIA GPUs. Fujitsu claims higher inference throughput and reduced cooling power consumption, but the article notes that supporting benchmark details are unavailable. ### Source excerpt Fujitsu is bringing its 2nm FUJITSU-MONAKA processor to AI infrastructure with a new server family designed to run AI inference in air-cooled data centers without requiring specialized liquid cooling. The MONAKA Server is designed, developed, and manufactured in Japan, with component and manufacturing traceability for sovereign AI deployments. The first MONAKA Servers will come in The post Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November appeared first on StorageReview.com. ## d-Matrix Joins the NVIDIA NVLink Fusion Platform DevFeed: [d-Matrix Joins the NVIDIA NVLink Fusion Platform]() Original publisher: [Read original article]() Author: Cliff Robinson Published: 2026-09-12T21:42:59Z Content type: news Language: en Sources: [ServeTheHome]() Topics: [d-matrix](), [NVLink](), [Nvidia](), [xpu](), [AI Inference](), [datacenter](), [networking](), [cpu](), [Spectrum-X]() Tags: [accelerators](), [ai](), [ai-accelerator](), [ai-inference](), [d-matrix](), [data-center](), [ethernet](), [networking](), [nvidia](), [nvidia-vera](), [nvlink](), [server](), [xpu]() ### AI overview d-Matrix and NVIDIA announced that d-Matrix will bring its next-generation XPUs to the NVLink Fusion platform. The integration is intended to support scaling from individual Raptor XPUs to larger rack-scale and clustered deployments for AI inference, alongside NVIDIA networking and CPU technologies. ### Source excerpt d-Matrix and NVIDIA announced that d-Matrix will use NVLink Fusion to scale up and out with its next-gen Raptor AI accelerators The post d-Matrix Joins the NVIDIA NVLink Fusion Platform appeared first on ServeTheHome. ## 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 [...] ## 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 +... ## Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data DevFeed: [Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-03T16:22:15Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [Inference Performance](), [AI Infrastructure](), [model-serving](), [Open Source Models & Datasets](), [data centers](), [networking]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [architectures](), [data-centers](), [enterprise](), [hardware](), [inference](), [latency](), [networking](), [open-source](), [production]() ### AI overview Equinix Inference Exchange is a distributed AI inference platform that places NVIDIA compute and Together AI's open-model serving closer to enterprise data, users, and applications. It combines Equinix's interconnection infrastructure, NVIDIA hardware, and support for more than 200 open-source models to address latency, data sovereignty, networking complexity, and inference costs. ### Source excerpt Equinix has expanded its partnership with NVIDIA and entered a new collaboration with Together AI to launch Equinix Inference Exchange. Designed as a distributed AI inference architecture for enterprise deployments, the platform aims to shift compute workloads closer to core data repositories, end users, and operational applications. Announced alongside Equinix Fabric One at the Equinix The post Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data appeared first on StorageReview.com. ## How to Size GPUs for AI Inference and TCO Without Overspending DevFeed: [How to Size GPUs for AI Inference and TCO Without Overspending]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-01T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Inference](), [GPU](), [Inference Performance](), [Large Language Model](), [cloud-infrastructure](), [Optimization]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [concurrency](), [cost](), [data-center-cloud](), [gpu](), [inference-performance](), [latency](), [llm](), [mlops](), [quantization]() ### AI overview A practical guide to sizing GPU infrastructure for AI inference workloads while balancing latency, concurrency, model choice, deployment strategy, and total cost of ownership. ### Source excerpt The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently... ## LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs DevFeed: [LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs' Probabilistic Beliefs]() Original publisher: [Read original article]() Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [Uncertainty quantification LLMs](), [AI Inference](), [Data Science]() Tags: [ai](), [data-science](), [diagnostics](), [llms](), [research]() ### AI overview This research examines whether large language models update probabilistic beliefs in accordance with Bayes' rule. It introduces the information processing gap to quantify deviations from Bayesian updates, compares evidence-integration approaches, and finds that heuristic, non-Bayesian updates can outperform exact Bayesian updates on downstream tasks. ### Source excerpt Modern AI systems are being deployed in complex domains such as medicine, science, and law, where there is often not a single correct answer given the observed evidence. Such systems must be able to represent and update uncertain beliefs about the world as new evidence arrives to make rational decisions. We introduce the novel technique of studying LLMs as information processing rules and utilize the information processing gap--the deviation from Bayes updates--to study the internal (in)consistencies of how LLMs update their probabilistic beliefs from evidence. Our extensive experiments evaluate... ## LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation DevFeed: [LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-22T11:44:27Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Large Language Model](), [AI Inference](), [Benchmark](), [GPU](), [Latency]() Tags: [ai-engineer](), [batching](), [gpu](), [inference](), [latency](), [llm]() ### AI overview A developer guide explains how static, dynamic, and continuous batching affect LLM inference throughput, latency, and GPU utilization. It also identifies chunked prefill and prefill-decode disaggregation as additional serving strategies. ### Source excerpt Static, Dynamic, and Continuous batching, Chunked prefill, and Prefill-Decode disaggregation, simply explained. ## How we think about text classification in the LLM era DevFeed: [How we think about text classification in the LLM era]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2026-08-19T20:00:37Z Content type: article Language: en Sources: [Medium]() Topics: [Machine Learning & Artificial Intelligence](), [Model Development](), [LLM Techniques](), [Artificial Intelligence](), [AI Inference]() Tags: [ai](), [classification](), [inference](), [llm](), [llms](), [machine-learning](), [model](), [models](), [recommendation-system](), [text-classification]() ### AI overview Medium explains how it is evaluating LLM-based text classification for updating its aging NSFW model while retaining task-specific machine-learning models. The article states that Snowflake LLM tools were used for inference only and that Medium's user data was not used to train the models. ### Source excerpt Why we think LLMs can be useful and why we will not replace all of our models with themContext At Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers. Here's some examples: a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policy Before we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general. Medium has been training internal models with user and post data for a long time now. We train models with specific tasks. For example, models that power our recommendations algorithm, or text classification models like the ones presented in this story. All in the goal to improve our product. With the LLM approach I describe in this story, we ARE NOT sharing these models with other companies. And we ARE NOT allowing anyone to train on our users' data and content. Here we used Snowflake LLM tools for inference only (no LLM training was done here) and they are actually hosting all of the models inside their own infrastructure and guarantee that they are not using any of this for training. Shoutout to the Snowflake team for making it so easy and safe to use LLMs on our data! If you want to read more about Medium's stance on AI, I definitely recommend giving these a read: Default No to AI Training on Your Stories Finally, an internet standard for writers' rights vs. AI companies We want your feedback: How can writers use AI to tell human stories? Problem During our roadmap planning we decided that our NSFW model was out of date and it was time to revamp it. This model labels stories as "Not Safe for Work" if they have sexually explicit content, lots of profanity, or basically anything you wouldn't want to read on your big monitor in the middle of an open space! As you can imagine it's a pretty important model. We really need it to make sure our most "interesting" conte ## How Google is Making Private AI Practical with Homomorphic Encryption DevFeed: [How Google is Making Private AI Practical with Homomorphic Encryption]() Original publisher: [Read original article]() Author: Jeremy Kun Published: 2026-08-14T14:00:00Z Content type: article Language: en Sources: [Security]() Topics: [Encryption](), [Artificial Intelligence](), [Google](), [AI Inference](), [Compiler](), [Cryptography](), [toolchain](), [Open Source](), [Security](), [Cloud]() Tags: [ai](), [ai-inference](), [cloud](), [cryptographic](), [data](), [encryption](), [google](), [none](), [open-source](), [security](), [toolchain]() ### AI overview Google introduces HEIR, an open-source compiler toolchain designed to make private AI inference practical with homomorphic encryption. The approach lets servers compute on encrypted data and return encrypted results without exposing the underlying information, while addressing the usability challenges of adopting homomorphic encryption. ### Source excerpt heir logo ## NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation DevFeed: [NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-11T19:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [SDKs](), [CUDA](), [GPU](), [AI Inference](), [Inference Performance](), [Latency](), [Low Latency](), [C++](), [Python](), [Artificial Intelligence](), [Automation]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [automation](), [c-plus-plus](), [cuda](), [edge-computing](), [featured](), [gpu](), [inference](), [jetpack](), [jetson](), [latency](), [low-latency](), [nvidia](), [python](), [robotics](), [robotics-compute](), [sdks](), [video-analytics](), [video-codec-sdk]() ### AI overview NVIDIA JetPack 7.2.1 adds PyNvVideoCodec 2.2 support on Jetson Thor, enabling Python-based hardware video encoding and decoding with GPU-resident frames. It also introduces agentic video skills that turn developer goals into device inspection, configuration, execution, measurement, and evidence-driven codec workflows. ### Source excerpt Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media... ## Lessons from Teams Running High-Volume AI Inference in Production DevFeed: [Lessons from Teams Running High-Volume AI Inference in Production]() Original publisher: [Read original article]() Author: Hasan Nabulsi Published: 2026-07-02T10:00:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [AI Inference](), [Inference Performance](), [Artificial Intelligence](), [Latency](), [Orchestration](), [Security](), [Architecture & Design]() Tags: [2026](), [ai](), [ai-inference](), [ai-ml](), [architecture](), [inference](), [latency](), [orchestration](), [performance](), [security](), [video]() ### AI overview This article summarizes lessons from engineering leaders at Workato, Hippocratic AI, and ISMG on operating AI inference in production. It focuses on latency management, orchestration, agent permissions, governance, security guardrails, and infrastructure decisions needed to move from prototypes to reliable high-volume systems. ### Source excerpt Moving AI from a flashy demo to a high-volume production environment is a transition filled with hidden technical debt and infrastructure challenges. There's a difference between calling the OpenAI API in a weekend prototype and serving 50,000 concurrent users who need sub-200ms latency, graceful fallbacks, and reliable output every single time. It is rarely a "model problem." Instead, it is a problem of decisions, trade-offs, and architecture. At DigitalOcean Deploy 2026, we hosted a panel of engineering leaders from Workato, Hippocratic AI, and ISMG. Moderated by Karnik Modi, DigitalOcean's Senior Manager of Engineering, panelists shared the lessons they've learned while running inference workloads at scale. The session focused on managing P99 latency spikes in real-time interactions, restricting agent permissions to prevent "admin" vulnerabilities, and ensuring infrastructure is policy-aware before production traffic hits. These insights move beyond model performance to address the orchestration and security guardrails required for reliable, mass-scale AI. Watch the full recorded session from Deploy 2026: View YouTube video The Built for Mass Scale Panelists Each panelist represents a company operating at the frontier of production AI, where the gap between a working prototype and a reliable system serving real users is the entire challenge. From orchestrating autonomous agents across thousands of enterprise applications to running real-time clinical voice conversations where latency is a patient-safety issue to deploying AI-powered intelligence across a global cybersecurity media network, these teams have confronted the infrastructure, governance, and architectural decisions that only surface at scale. Oscar Wu -- AI Research Technical Lead, Workato Research Lab Workato is an enterprise integration platform that connects over 14,000 applications and has orchestrated more than one trillion automated tasks, and its AI focus has shifted to agentic orchestration'--bui ## Introducing TabFM: A zero-shot foundation model for tabular data DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data]() Original publisher: [Read original article]() Published: 2026-06-30T10:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [Machine learning](), [Google](), [Feature Engineering](), [Hyperparameter optimization](), [Language models](), [BigQuery](), [GitHub](), [hugging face](), [AI Inference]() Tags: [bigquery](), [classification](), [data](), [data-management](), [feature-engineering](), [github](), [google](), [hugging-face](), [hyperparameter-optimization](), [machine-intelligence](), [model](), [product](), [zero-shot]() ### AI overview Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery. ### Source excerpt Data Management ## Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction DevFeed: [Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction]() Original publisher: [Read original article]() Published: 2026-06-26T18:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Inference](), [On-device AI](), [Google](), [LLMs](), [gemma]() Tags: [ai](), [efficiency](), [energy](), [gemini](), [inference](), [machine-intelligence](), [mobile-systems](), [natural-language-processing](), [on-device](), [on-device-ai](), [phones]() ### AI overview Google Research describes a method for retrofitting Multi-Token Prediction onto frozen Gemini Nano v3 production models to accelerate on-device inference on Pixel phones. The approach targets mobile energy and memory constraints, improving the speed and energy efficiency of features such as notification summaries and proofreading without requiring separate drafting models. ### Source excerpt Machine Intelligence ## Kubernetes teams trust automation to ship code but not to touch CPU, and AI is raising the stakes DevFeed: [Kubernetes teams trust automation to ship code but not to touch CPU, and AI is raising the stakes]() Original publisher: [Read original article]() Author: Yasmin Rajabi Published: 2026-06-23T20:56:47Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Kubernetes](), [Optimization](), [autoscaling](), [CI/CD](), [Deployment](), [AI Inference]() Tags: [ai-inference](), [ai-infrastructure](), [autoscaling](), [ci](), [ci-cd](), [contributed](), [contributed-cloudbolt](), [deployment](), [finops](), [inference](), [kubernetes](), [optimization]() ### AI overview A survey of 321 enterprise Kubernetes practitioners finds a sharp trust gap between automated software delivery and automated resource optimization. While 82% report high or complete trust in delivery controls, 71% require human review for resource optimization recommendations and only 27% allow CPU and memory changes to be applied automatically. The article explains that rightsizing is perceived as riskier because it changes scheduling and runtime resource constraints. ### Source excerpt Kubernetes teams automate deployments without thinking about it. CI/CD pipelines fire dozens of times a day, autoscaling adjusts replicas in The post Kubernetes teams trust automation to ship code but not to touch CPU, and AI is raising the stakes appeared first on The New Stack. ## Managing Agentic AI Costs at Scale DevFeed: [Managing Agentic AI Costs at Scale]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [AI Agent](), [agentic workflows](), [Claude Code](), [AI Inference]() Tags: [agentic-ai](), [ai-agents](), [claude-code](), [cost](), [engineering](), [inference]() ### AI overview This article examines why production agentic AI can cost substantially more than pilot deployments or standard chatbot use. It argues that total task cost includes planning, context retrieval, tool calls, state management, validation, and retries, and discusses building a business case before costs escalate. ### Source excerpt What do the Uber budget blowout, a 24x token multiplier, and context teach us about building a real business case for AI Agents in production? ## OpenCode Now Supports DigitalOcean Inference Router for Intelligent Model Routing DevFeed: [OpenCode Now Supports DigitalOcean Inference Router for Intelligent Model Routing]() Original publisher: [Read original article]() Author: Musa Malik Published: 2026-05-28T21:02:42Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Model Routing](), [AI Inference](), [AI-assisted coding](), [Open Source](), [coding]() Tags: [ai-coding](), [ai-inference](), [api](), [cost](), [digitalocean](), [latency](), [model-routing](), [open-source](), [product-updates]() ### AI overview DigitalOcean's Inference Router is in public preview and can be accessed through OpenCode, an open-source AI coding agent. It dynamically routes requests across models to help developers manage latency, cost, and output quality. ### Source excerpt Coding agents today have a massive spending problem. Every request, whether you're designing system architecture or writing a single-line docstring, often gets routed to the same expensive frontier model. The result: unnecessary token usage, higher inference costs, and little awareness of task complexity or budget constraints. This high cost stems from a "one-size-fits-all" approach to model usage, where premium frontier models are utilized for trivial tasks that don't require such intensive reasoning effort. In multi-agent workflows, where orchestrators delegate work to specialized subagents, this lack of discrimination frequently leads to runaway costs and opaque failure modes. Without intelligent routing, developers can essentially be forced into closed-provider lock-in and high API usage fees, which quickly escalate during exploratory building phases. DigitalOcean Inference Router, now in Public Preview, was built to solve this problem by dynamically routing requests to the right model for the job. As part of DigitalOcean's AI-Native Cloud, it gives developers a unified way to control, optimize, and evaluate AI inference across models. And as of today, you can access it through OpenCode, the open-source AI coding agent, in as little as a few seconds. What is an Inference Router? An Inference Router is the auto-mode pattern engineers are used to, but with deliberate control over the tradeoffs that matter: latency, cost, and output quality. Rather than statically pointing your coding agent to a single model, an Inference Router can analyze each request and route it to the model best suited for that specific task. Not the most powerful model available, but the right model. That distinction is what drives real savings without compromising on your desired quality of output. To use DigitalOcean's Inference Router: Create an Inference Router from the router catalog--pick a preset or build a custom router via the API or UI. No GPU management, no infrastructure to run. Us ## ML based ranking using Nrtsearch DevFeed: [ML based ranking using Nrtsearch]() Original publisher: [Read original article]() Author: Mohammad Mohtasham (Software Engineer); Tao Yu (Software Engineer) Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Yelp]() Topics: [Machine Learning & Artificial Intelligence](), [Inference](), [AI Inference](), [model-deployment](), [backends](), [Open Source]() Tags: [blog-post](), [inference](), [latency](), [machine-learning](), [ml](), [models](), [network](), [open-source](), [performance](), [pipeline](), [ranking](), [software]() ### AI overview Yelp extended its Lucene-based Nrtsearch engine with an Inference Plugin that embeds machine-learning ranking directly in the search layer. The article explains the ranking workflow, including model configuration and loading, and describes how co-locating feature storage and inference reduces network transfer, serialization overhead, and latency compared with a standalone inference service. ### Source excerpt We've extended Nrtsearch with the Inference Plugin, which embeds ML-based ranking directly in the search layer -- eliminating the need for a standalone scoring service. We use Nrtsearch (read more information on the blog post), a Lucene-based open-source search engine built by Yelp, to power a variety of applications such as business search, reviews search, ad delivery and photo search. In this blog post, we give a high-level overview of the Machine Learning (ML) based scoring workflow in Nrtsearch. We'll show how ML models are configured and loaded, and how different applications use custom business logic to develop, test, and... ## NetEase Games reduced LLM model load time to 3 minutes with Fluid prefetching DevFeed: [NetEase Games reduced LLM model load time to 3 minutes with Fluid prefetching]() Original publisher: [Read original article]() Author: Haifeng Liao Published: 2026-05-06T13:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Large Language Model](), [AI Inference](), [AI Platform](), [autoscaling](), [Kubernetes](), [Cache](), [Serverless](), [GPU]() Tags: [ai-infrastructure](), [ai-platform](), [autoscaling](), [cache](), [cncf](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [latency](), [llm](), [post-contributed](), [serverless](), [sponsor-cncf](), [sponsored-post-contributed]() ### AI overview NetEase Games describes how slow model loading limited serverless LLM inference across regions. Using an Alluxio-based cache and then Fluid's prefetching workflow, the representative model load time fell from 42 minutes to 3 minutes. ### Source excerpt At NetEase Games, we learned a hard lesson about large language model (LLM) inference in production: elastic compute is only The post How NetEase Games cut LLM cold starts from 42 minutes to 30 seconds appeared first on The New Stack. ## OpenCL Cooperative Matrix Extensions Are Here DevFeed: [OpenCL Cooperative Matrix Extensions Are Here]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-04-29T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [AI Inference](), [GPU](), [C](), [LLVM](), [GitHub]() Tags: [api](), [arm](), [blog-opencl-spirv-machinelearning-llv](), [c](), [gpu](), [intel](), [llvm](), [opencl](), [qualcomm](), [spir](), [vulkan]() ### AI overview The OpenCL Working Group has published a working draft of the cl_khr_cooperative_matrix extension, developed with Arm, Intel, and Qualcomm. The extension brings cooperative matrix operations for ML inference to OpenCL, while a companion OpenCL C extension is also being developed. Community feedback is requested before the standards are finalized. ### Source excerpt The OpenCL Working Group has published a draft extension (cl_khr_cooperative_matrix) that brings cooperative matrix operations--a key technology for accelerating ML inference--to OpenCL, developed in collaboration with Arm, Intel, and Qualcomm. A companion extension to expose these capabilities directly in the OpenCL C language is also in progress, and the community is invited to review both drafts and provide feedback before they are finalized. ## DeepInfra on Hugging Face Inference Providers 🔥 DevFeed: [DeepInfra on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Aray Sultanbekova; Shang-Pin; Utemuratov; Yessen K; Oguz Vuruskaner; Célina Hanouti; Simon Brandeis; Lucain Pouget Published: 2026-04-29T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepinfra](), [hugging face](), [inference-providers](), [AI Inference](), [SDKs](), [Large Language Model](), [API keys](), [text-generation](), [text-to-image]() Tags: [ai-inference](), [api](), [api-keys](), [applications](), [artificial-intelligence](), [code](), [deepinfra](), [enterprise](), [hub](), [hugging-face](), [inference](), [inference-providers](), [javascript](), [llms](), [open](), [open-source](), [partnerships](), [pricing](), [python](), [sdks](), [text-generation](), [text-to-image]() ### AI overview DeepInfra is now a supported Inference Provider on the Hugging Face Hub, with serverless access to more than 100 models and integration with Hugging Face's JavaScript and Python SDKs. The article describes provider selection, API-key and routed-by-Hugging-Face modes, supported model tasks, and initial access to conversational and text-generation models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The LLM Inference Trilemma: Throughput, Latency, Cost DevFeed: [The LLM Inference Trilemma: Throughput, Latency, Cost]() Original publisher: [Read original article]() Author: Balaji Varadarajan Published: 2026-04-22T15:56:14Z Content type: tutorial Language: en Sources: [DigitalOcean]() Topics: [Large Language Model](), [AI Inference](), [Latency](), [Optimization](), [benchmarking](), [Hardware](), [hosting]() Tags: [benchmarking](), [compute](), [cost](), [engineering](), [hardware](), [inference](), [infrastructure](), [latency](), [llm](), [optimization]() ### AI overview This practical guide explains the tradeoff between throughput, latency, and cost when serving large language models. It examines why LLM inference differs from traditional web-service scaling, how inference costs extend beyond token pricing, and how hardware selection and benchmarking inform deployment decisions. ### Source excerpt We know how to scale traditional web services: throw a load balancer in front of stateless microservices and horizontally scale your CPU instances as traffic grows. Large Language Models break this playbook because LLM inference is fundamentally stateful, bottlenecked by memory bandwidth rather than raw compute, and bound to physical hardware interconnects. Scaling LLM inference isn't just a matter of adding more servers; it's a delicate, multi-dimensional optimization problem. Classic case of "Trilemma" If you've served a large language model in production, you've encountered the trilemma. Push throughput up, and latency creeps higher. Clamp latency down, and your GPU bill inflates. Try to optimize cost, and you're forced to make uncomfortable compromises on one of the other two dimensions. This three-way orthogonal tension--throughput, latency, cost--is the central engineering challenge in dedicated LLM hosting. Understanding it deeply is the difference between a system that helps scale with economics in mind and one that increases your infrastructure budget. This article is a practitioner's guide to navigating these trade-offs. We'll unpack what "cost" actually means in the inference world (spoiler: it's not just $/token), walk through the levers that dictate cost, and discuss how hardware selection and benchmarking expose the real cost surface. Finally, we'll touch on when and why you might optimize for throughput versus latency and what that decision costs you. What Does "Cost" Actually Mean in LLM Inference In standard web hosting, cost is often linear (more traffic = more servers). In LLM hosting, "cost" is a multi-dimensional metric. When people talk about inference costs, they usually default to a single number--dollars per million tokens. While running dedicated infrastructure, the real cost of serving an LLM is a composite of at least four distinct dimensions. Capital Cost (CapEx): Paying for the Full Node This is the hardware cost. Because GPUs are tied tog [Next page]()