Search: “digitalocean”

DigitalOcean announced a new AI-native cloud platform designed to support production artificial intelligence workloads with optimized infrastructure and services. The offering likely provides speciali

DigitalOcean discusses infrastructure and platform capabilities designed to support the inference phase of machine learning, where trained models are deployed to make predictions on new data at scale.

This article profiles the team and processes behind DigitalOcean's Deploy product, which enables users to ship AI applications to production. It likely covers the engineering approach, team structure,

DigitalOcean's serverless inference offering enables developers to deploy and run machine learning models without managing underlying infrastructure, automatically scaling resources based on demand. T

OpenCode now integrates with DigitalOcean's Inference Router, enabling intelligent routing of AI model requests across distributed infrastructure. This integration allows developers to optimize model

DigitalOcean introduced a unified batch inference solution designed to enable scalable and cost-efficient AI model inference workloads. The offering allows users to process multiple inference requests

DigitalOcean describes the architecture and engineering approach behind their Inference Router, a system designed to route and manage machine learning inference requests efficiently across distributed

DigitalOcean outlines its AI-native cloud infrastructure designed to support the inference phase of AI model deployment, emphasizing tools and services that enable businesses to run and scale AI model

DigitalOcean describes their optimization and deployment of three large language models (DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B) on their Serverless Inference platform, likely leveraging NVIDI

DigitalOcean's Dedicated Inference offering provides cloud infrastructure optimized for running machine learning inference workloads with guaranteed resources and performance isolation. This technical

DigitalOcean's Inference Cloud Memory Layer is a technical architecture component designed to optimize database performance by implementing an in-memory caching layer for faster data access and reduce

DigitalOcean's Gradient AI Platform enables developers to build intelligent documentation agents that can automatically generate, maintain, and query technical documentation using large language model

DigitalOcean has released a public preview of Managed Weaviate, a fully managed vector database service that enables users to scale AI and machine learning applications without managing infrastructure

DigitalOcean has released Evaluations, a feature for testing production models and routers within their inference stack. This tool enables developers to validate and benchmark their AI/ML models befor

NVIDIA and DigitalOcean are collaborating to develop an open infrastructure stack designed to support autonomous AI agents that operate continuously. The initiative emphasizes open-source principles a

DigitalOcean transitioned from traditional incident counting metrics to Service Level Indicators (SLIs) for measuring and managing availability, adopting a more nuanced approach to understanding syste

Kimi K3 is now available on @digitalocean 's Serverless Inference! Developers can start building with our most capable model in minutes. .@Kimi_Moonshot K3 from Moonshot AI is now live on DigitalOcean

DigitalOcean launched Kimi K3 on day 0. It’s already one of the most popular models on the platform and across the market: second most likes on Hugging Face, sixth most traffic on OpenCode. Getting a

Shares of DigitalOcean Holdings Inc. rocketed more than 40% today after the developer-oriented cloud infrastructure provider topped Wall Street targets in its fiscal 2026 first quarter. It also lifted

Project Poseidon is DigitalOcean's initiative focused on achieving zero-downtime reliability in cloud infrastructure and services. The project addresses the technical challenges of maintaining continu

Effective August 1st, 2026, we will be updating prices on select GPUs. This change reflects strong demand for advanced GPU capacity and helps us expand reliable access to high-performance compute for

DigitalOcean shares insights from rapidly scaling their engineering team by hiring 33 engineers in a two-week period, likely covering recruitment strategies, interview processes, and lessons learned f

This DigitalOcean blog post examines how emerging AI technologies are transforming business models and enabling startups to challenge traditional industries. It likely covers practical examples of AI-

DigitalOcean has made request-based autoscaling generally available on its App Platform, enabling applications to automatically scale based on incoming HTTP request volume rather than just CPU or memo

This DigitalOcean guide covers strategies and best practices for deploying and optimizing very large language models (600 billion+ parameters) on cloud infrastructure, focusing on inference performanc

This article shares practical lessons and best practices from teams operating large-scale machine learning inference systems in production environments. It covers challenges and solutions related to m

This article discusses methods and tools for evaluating routing policies in machine learning models, likely covering techniques to validate that model routing decisions are effective and functioning a

Load balancing and scaling LLM serving involves distributing inference requests across multiple model instances or GPUs to prevent bottlenecks and ensure consistent response times under varying traffi

This article discusses strategies for optimizing the performance of advanced AI frontier models when running on AMD hardware infrastructure. It likely covers deployment best practices, hardware config

This article discusses how prefix-aware routing and prefix caching techniques can reduce the computational overhead and costs associated with running large language models at scale by eliminating redu

This article argues that infrastructure quality and architecture are more critical to AI/ML project success than the choice of underlying model. It likely explores how proper deployment, scaling, moni

This article examines the fundamental trade-offs in large language model inference operations, specifically the competing priorities of maximizing throughput, minimizing latency, and reducing costs. I

Prompt caching delivers significant efficiency gains at a single replica, but under standard round-robin load balancing, a request with an identical prefix has only a 1/N chance of hitting the repl...

https://github.com/NVIDIA-NeMo/Switchyard Finally an open source LLM router. An alternative to openrouter fusion and Sakana Fugu. Doesn't look like it does exactly what Sakana Fugu does according to i

💡The world needs an open-source platform, and that’s exactly what we’re building to give our customers more choice and the flexibility to choose the right model for the right task. As part of this, we

Automated lint: 20 errors, 8743 warnings, 3 info

Automated lint: 26 errors, 6728 warnings, 3 info

Database powerhouse Oracle Corp. is making its case that database architecture must serve as a trusted foundation for the AI that gets built on top. The company’s major step into the AI database as a