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Higher Education and Academic Research

Research and learning without limits - Composable Infrastructure in higher education

Academic institutions lead the way in discovering next-generation emerging technologies, accelerated computing, data science, and AI.

With limited budgets, higher education institutions must balance the demand for high-performance computing infrastructure with scarce resources. Liqid's composable infrastructure delivers flexibility and performance to deliver discoveries without limits.

Benefits

Composable infrastructure enables universities to achieve higher performance by providing a flexible, scalable, and efficient way to manage and allocate computing resources dynamically. By embracing composable infrastructure, universities can maximize resource efficiency, accelerate groundbreaking research, and provide their faculty and students with unparalleled computational capabilities—all while keeping costs under control.

Flexibility

Researchers and faculty can spin up or tear down environments quickly, fostering agility in academic and research projects.

IT teams can easily provision and manage resources for diverse research initiatives, streamlining operations and enhancing collaboration efficiency.

Performance

By directly connecting resources over high-speed fabric networks, composable systems reduce latency and increase throughput, crucial for tasks like deep learning, genomic sequencing, and climate simulations.

As research demands increase, composable infrastructure allows universities to scale up by simply adding new hardware to the pool, eliminating the need for disruptive infrastructure overhauls.

Efficiency

Instead of over-provisioning or underutilizing hardware, resources are optimized across multiple departments and projects, reducing waste and maximizing ROI.

Researchers can easily reconfigure and test new setups without needing dedicated hardware, accelerating the pace of discovery and innovation.

Use Cases

Research and Simulations
Enable simulations and analyses in physics, chemistry, biology, and other fields that require massive computational power pooled and shared across research teams. Examples include:
  • Climate modeling: Simulating global climate patterns to study environmental changes.
  • Genomic sequencing: Accelerating research in genetics and personalized medicine.
Big Data Analysis
Analyze enormous datasets across disciplines leveraging GPU dense computational power for:
  • Social sciences: Processing large-scale surveys or social media data for trends and behavioral insights.
  • Healthcare studies: Analyzing medical records for disease trends or drug efficacy.
Artificial Intelligence and Machine Learning
Training complex AI and ML models in academic applications.
  • Natural language processing: Research in linguistics and automated translation.
  • Autonomous systems: Developing and testing algorithms for robotics and autonomous vehicles.
  • Education tools: Improving adaptive learning systems through real-time data analysis.
Advanced Engineering and Design
Support detailed simulations and optimizations in engineering, such as:
  • Aerodynamics: Testing and designing aircraft or automotive systems.
  • Materials science: Simulating molecular structures for new materials or drug development.
  • Energy systems: Modeling renewable energy grids and optimizing power distribution.
Education and Training
Enhance teaching and skill development through:
  • Virtual labs: Offering students access to computational resources for experiments.
  • Courses in computational sciences: Hands-on experience with HPC platforms for future professionals.
  • Interdisciplinary learning: Providing tools for collaboration in computational biology, data science, and digital humanities.
Generative AI
Accelerate the work of researchers who are revolutionizing higher education to create.
  • Research and publication through text, images, sounds, animations, 3D models, and code content creation, and collaboration across various academic areas of study.

Let’s Get Started

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