ASUS and Poesis Team Up on Autonomous Trading Agents Powered by NVIDIA Technologies

ASUS and Poesis Team Up on Autonomous Trading Agents Powered by NVIDIA Technologies

Test Offers a Glimpse of Possibilities for Agents Using Live Capital on Deskside Supercomputer

KEY POINTS

• Autonomous AI agents trade in live financial markets: ASUS and Poesis completed a week-long experiment in which agentic AI agents independently conducted investment research, managed risk, and executed trades using live capital and predefined mandates.
• A deskside supercomputer powers the entire AI workflow locally: Running on the ASUS ExpertCenter Pro ET900N G3, powered by NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, the experiment demonstrated local execution of AI models, agents, and workflows without dependence on cloud-based AI infrastructure.
• Exploring the future of agentic AI in asset management: The collaboration offers early insights into how local, high-performance computing can support autonomous investment workflows and the ongoing development of agentic AI systems for financial markets. 

ASUS and Poesis today announced the results of a week-long experiment that successfully deployed agentic AI to trade autonomously in financial markets. The system ran entirely on the ASUS ExpertCenter Pro ET900N G3, a deskside supercomputer built on the NVIDIA DGX Station platform, powered by NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip.

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The ASUS ExpertCenter Pro ET900N G3 deskside AI supercomputer is now shipping. It is powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip and delivers up to 20 PFLOPS of AI performance with 748GB of coherent memory. The ET900N G3 is built for secure enterprise AI, so organizations can develop, fine-tune, and run large AI models on their own premises.

Throughout the test, all models, agents, and workflows ran locally on the ASUS ExpertCenter, showing that agents can function in live markets without relying on cloud-based AI infrastructure.

"While this was an early-stage experiment, it showed that agents can operate continuously in live markets while remaining within clearly defined constraints," said Alex Popa, Founder and CEO of Poesis, an AI-native asset manager developing agentic systems for financial markets.

The collaboration reflects growing interest across industries in moving agentic AI from experimentation towards practical development, particularly in asset management.

“Working alongside Poesis at the intersection of AI and financial markets gave us valuable insights,” said Yen Hoang, Director of Marketing, B2B, ASUS North America. “As agentic AI continues to evolve, projects like this are essential to helping us better understand how autonomous investment workflows can be developed and deployed in real-world trading environments.”

Built on NVIDIA DGX Station Powered by NVIDIA Blackwell Ultra

The week-long experiment ran on the ASUS ExpertCenter Pro ET900N G3, powered by NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip. Featuring up to 20 petaFlops of AI performance and 748GB of coherent memory, the system enabled Poesis to deploy a multi-agent AI workflow locally on a single machine.

Using live capital, the agents autonomously handled investment research, risk management, and trade execution while operating within predefined mandates.

Explore ASUS ExpertCenter Pro ET900N G3: https://us.asus.click/et900ng3

About ASUS

ASUS (2357.TW) is an innovative technology leader delivering the world’s most comprehensive AI solutions across Infrastructure, Physical AI, AI PC/Devices, and Engineering the AI Advantage. Guided by its “Ubiquitous AI. Incredible Possibilities” strategy, ASUS is bringing enterprise-to-edge AI to life. The ASUS portfolio also includes the ROG and ProArt sub-brands, which serve gamers and creators worldwide.

About Poesis

Poesis is an AI-native asset manager developing agentic systems that research, manage risk, and trade, deploying AI-led investment strategies in live markets. Poesis was founded by Alex Popa, previously a partner and portfolio manager at Capital Group, and Charles Elkan, former Global Head of Machine Learning at Goldman Sachs.

Disclaimer

This experiment was conducted as a limited-duration proof of concept designed to evaluate the operational characteristics of autonomous AI agents in a live market environment. The results should not be interpreted as evidence of long-term investment performance or future financial outcomes. References to trading activity are provided solely to describe the experimental workflow and do not constitute investment advice or recommendations.

Media contact information
Email: morris_shao@asus.com

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