Baolande AIOS Intelligent Computing Platform Accelerates AI for Science at a Leading Research Institute

Customer: A prominent research institute dedicated to advancing scientific discovery through computational and AI-driven methodologies, supporting researchers, faculty, and students across multiple disciplines.

Industry: Scientific Research / Higher Education

Challenge

The institute had built an AI computing platform using heterogeneous compute resources to support AI projects and research activities. However, several critical challenges hindered productivity and efficiency:

• Fragmented compute infrastructure – Diverse hardware accelerators from multiple vendors operated in silos, each with its own driver stacks and management interfaces, leading to low overall utilisation and long wait times for researchers.
• Inefficient development workflows – Researchers spent excessive time setting up environments, managing dependencies, and porting algorithms across different hardware types, diverting effort away from scientific discovery.
• Complex lifecycle management – From algorithm development and model training to inference deployment and agent construction, the full AI R&D pipeline required disparate tools and manual handoffs, slowing iteration cycles.
• Limited visibility and governance – Without comprehensive monitoring and resource accounting, the institute struggled to track compute consumption, identify idle capacity, or plan capacity investments effectively.

Objective

The institute sought a next-generation intelligent computing platform that would:

• Unify heterogeneous compute resources under a single scheduling framework, abstracting away hardware complexity.
• Streamline the entire AI R&D lifecycle – from development and training to deployment and agent orchestration – through an integrated, self-service environment.
• Accelerate scientific output by minimising infrastructure overhead and enabling researchers to focus on domain-specific innovation.
• Provide elastic scalability to handle both small exploratory experiments and large-scale distributed training workloads.
• Enable granular resource governance with usage tracking, multi-dimensional analytics, and proactive optimisation to maximise return on compute investment.

Solution

The institute deployed the Baolande AIOS Intelligent Computing Integration Platform – a purpose-built system designed to address the unique demands of AI-driven research.

Unified Heterogeneous Scheduling – AIOS abstracts away differences across diverse hardware accelerators, presenting all compute resources as a cohesive pool. Researchers submit jobs without specifying target hardware; the platform intelligently matches workloads to the most appropriate available resources based on performance requirements and availability, transforming fragmented assets into a fluid, service-oriented environment.

End-to-End AI Development Toolchain – The platform delivers an integrated environment covering the full AI lifecycle:

• Algorithm development – Pre-configured workspaces with mainstream AI frameworks and development tools, enabling researchers to spin up isolated environments in minutes.
• One-stop model training and fine-tuning – Flexible resource configurations from single-node to distributed multi-node training, with automated checkpointing and fault recovery.
• Model inference optimisation – Unified serving for both large-scale and traditional models, including service management and call analytics.
• Agent Factory – A low-code environment for building, orchestrating, and deploying AI agents, complete with templates, knowledge bases, and tool libraries.

Open and Extensible Architecture – Dynamic adaptation interfaces and modular components allow the institute to rapidly integrate emerging AI technologies – including new training frameworks, inference engines, and engineering toolchains – ensuring the platform evolves with the fast-moving AI landscape.

Comprehensive Governance – Granular visibility into compute consumption, real-time monitoring, and proactive idle-resource alerts enable the institute to optimise utilisation, eliminate waste, and make data-driven capacity decisions.

Results

• Unified compute fabric – The platform has eliminated hardware silos, providing researchers across all disciplines with seamless access to a consolidated compute pool without navigating vendor-specific complexities.
• Accelerated research productivity – Pre-integrated frameworks, reusable templates, and self-service workspaces have reduced environment setup time from weeks to hours, significantly shortening the cycle from idea to execution across multiple scientific domains.
• Optimised resource utilisation – Intelligent scheduling and proactive monitoring have substantially improved overall compute utilisation, enabling more research to be conducted with the same physical infrastructure.
• Elastic scalability – The platform handles everything from small experiments to large-model distributed training, allowing researchers to scale workloads elastically based on immediate needs without over-provisioning.
• Future-ready foundation – The open, modular architecture ensures the platform can accommodate new hardware accelerators and AI innovations without re-architecture, protecting the institute's long-term investment.
• Strategic impact – Baolande AIOS has become the foundational compute layer for the institute's AI-driven research agenda. By abstracting infrastructure complexity and providing a unified, self-service environment, the platform enables scientists to focus on advancing knowledge – establishing a reference model for how research institutions can build agile, scalable, and efficient AI computing capabilities for the next generation of scientific discovery.