Qubrid AI Expands GPU Cloud and Previews Agentic Workbench for AI Agents

Qubrid AI unveils Version 3 of its AI GPU Cloud, featuring smarter model tuning, auto-stop deployment, and enhanced RAG UI—all designed to streamline AI workflows. The company also teased its upcoming Agentic Workbench, a new toolkit to simplify building autonomous AI agents. Along with App Studio and data provider integration, Qubrid is positioning itself as the go-to enterprise AI platform for 2025.
Qubrid AI Expands GPU Cloud and Previews Agentic Workbench for AI Agents
Image Credit: Qubrid AI

Qubrid AI, a leader in enterprise AI solutions, today announced a major update to its AI GPU Cloud Platform (V3), along with a robust roadmap featuring App Studio and the forthcoming Agentic Workbench – a transformative toolkit designed to simplify the creation and management of intelligent AI agents.


These updates reinforce Qubrid AI’s mission to democratize AI by enabling faster, smarter, and cost-effective AI development for enterprises, researchers, and developers. Users can access the new platform by visiting https://platform.qubrid.com

New Capabilities in AI GPU Cloud Platform V3:

  • New UI: A redesigned interface delivers an intuitive and seamless user experience, simplifying navigation and improving workflow management.
  • Model Tuning Page Optimization: Now directly accessible, this page allows users to select base models, upload datasets (CSV), configure parameters, and fine-tune models – all in a few clicks.
  • Chat History in RAG UI: Enhances the Retrieval-Augmented Generation experience by displaying past chat interaction – critical for debugging and context-aware improvements.
  • Auto Stop for Hugging Face Deployments: Enables users to automatically shut down deployed model containers after a defined duration, improving GPU utilization and reducing cost.

What’s Coming Next:

  • App Studio: A powerful, user-friendly workspace to design, prototype, and launch AI-powered applications within the Qubrid AI ecosystem.
  • Agentic Workbench: A toolkit purpose-built for developing and scaling AI agents that can autonomously perform tasks, make decisions, and adapt over time.
  • Data Provider Integration: Native integration into popular industry data providers solutions that allows easy access to proprietary data from GPU compute.

Empowering AI Innovation with Open Cloud Architecture

Qubrid AI’s Open Cloud architecture provides unmatched flexibility for Business Users, Product Managers, AI researchers and data scientists. By supporting popular open-source AI models and compatibility with Jupyter notebooks, the platform allows users to bring their own models, tools, and workflows while still benefiting from Qubrid’s high-performance GPU infrastructure.

No-Code Platform for Rapid AI Development

Qubrid AI’s no-code environment empowers both technical and non-technical users to build, train, and deploy models without writing a single line of code. With drag-and-drop interfaces, pre-configured templates, and guided workflows, users can go from idea to production-ready AI solutions in record time, dramatically shortening the innovation cycle with AI.

A Message from Qubrid AI’s CTO

“Our mission is to make advanced AI accessible, scalable, and enterprise-ready,” said Ujjwal Rajbhandari, Chief Technology Officer at Qubrid AI. “With this latest cloud release, we’re not just improving the user experience; we’re giving businesses the power to operationalize AI faster and more intelligently. From smarter resource management to frictionless model tuning and future-ready agentic tooling, we’re building the foundation for enterprise AI at scale.”

About Qubrid AI

Qubrid AI is a leading enterprise artificial intelligence (AI) company that empowers AI developers and engineers to solve complex real-world problems through its advanced AI cloud platform and turnkey on-prem appliances. For more information, visit http://www.qubrid.com/

Media Contact – Crystal Bellin
Email: digital@qubrid.com


Recent Content

In Q3 2025, Microsoft announced a robust revenue increase to $70.1 billion, driven by its cloud and AI segments. Highlights include a 20% surge in Microsoft Cloud revenue and Azure’s 33% growth, reflecting strong market demand for advanced cloud and AI capabilities.
Nvidia opposes the U.S. proposed AI chip export controls, highlighting potential negative impacts on innovation and global competitiveness. This article explores the differing views within the tech industry, focusing on the economic and strategic implications of such regulations.
As AI workloads explode in complexity and scale, telecom providers face a $1B+ opportunity to evolve from traditional carriers into AI connectivity enablers. This article explores how telcos can monetize AI-driven traffic through dynamic network infrastructure, edge AI hosting, and cloud-like billing models tailored to modern enterprise demands.
Artificial Intelligence is transforming metro infrastructure, placing new demands on data centers, fiber networks, and edge deployments. This article explores how operators are tackling power and cooling constraints, evolving network topologies, managing capital risks, and partnering with hyperscalers to build sustainable, AI-optimized metro ecosystems.
The fiber, data center, and telecom sectors are evolving rapidly amid rising AI workloads, cloud expansion, edge computing, and new investment models. This article breaks down the key trends — from fiber deployments in rural markets to secondary data center expansions and telecoms shifting to platform-based services, that are reshaping digital infrastructure for a hyperconnected future.
Huawei’s new AI chip, the Ascend 910D, has raised concerns about Nvidia’s China business, but analysts say it lacks the global performance, ecosystem, and efficiency to compete with Nvidia’s H100 GPU. Built on 7nm technology with limited software support, Huawei’s chip may gain local traction but poses no major international threat—yet.
Whitepaper
As VoLTE becomes the standard for voice communication, its rapid deployment exposes telecom networks to new security risks, especially in roaming scenarios. SecurityGen’s research uncovers key vulnerabilities like unauthorized access to IMS, SIP protocol threats, and lack of encryption. Learn how to strengthen VoLTE security with proactive measures such as...
Whitepaper
Dive into the comprehensive analysis of GTPu within 5G networks in our whitepaper, offering insights into its operational mechanics, strategic importance, and adaptation to the evolving landscape of cellular technologies....

It seems we can't find what you're looking for.

Download Magazine

With Subscription

Subscribe To Our Newsletter

Scroll to Top