About TensorRigs
I'm Abdullah Amawi, a Graduate Research Assistant specializing in AI/ML hardware optimization and high-performance computing systems. I created TensorRigs to help researchers, developers, and enthusiasts make informed hardware decisions for their AI workloads.
My Background
Academic Research
Graduate Research Assistant
University of Göttingen | 2022 - Present
- • Led AI and Swarm Intelligence Lab operations
- • Managed GPU infrastructure (PyTorch, TensorFlow, Jupyter)
- • Supported interdisciplinary AI research projects
- • Developed and maintained lab computing resources
Master's Thesis Research
Optimizing I/O Performance of Scalable ML Workflows in HPC Systems
Investigated data-loading bottlenecks in large-scale deep learning, achieving significant training time reductions through GPU-accelerated pipelines like NVIDIA DALI.
Industry Experience
Technical Sales & Consulting
15+ Years Experience | 2006 - 2021
- • Advised clients on high-performance computing hardware
- • Specialized in performance-focused system configurations
- • Consulted on hardware optimization for business workloads
- • Developed expertise in matching hardware to specific use cases
Education
Master of Science - Applied Computer Science
University of Göttingen
Specialization in Deep Learning, Cloud Computing, and High-Performance Computing Systems
Bachelor's Degree - Software Engineering
Why TensorRigs?
Research-Driven
Recommendations based on academic research, lab experience, and real-world HPC workloads
Practical Experience
15+ years in technical sales and consulting, plus hands-on lab infrastructure management
Spec-Driven Analysis
Focus on specifications, research data, and real-world performance rather than traditional benchmarking
My Approach
Unlike traditional hardware review sites that focus primarily on gaming benchmarks, I approach GPU recommendations from the perspective of actual AI and ML workloads. My recommendations are based on:
- • Academic research - Insights from managing university AI labs and infrastructure
- • Practical experience - Real-world optimization challenges in HPC environments
- • Spec-driven analysis - Focus on VRAM, tensor cores, and memory bandwidth for ML workloads
- • Cost-effectiveness - Understanding price-to-performance ratios for different use cases
- • Scalability considerations - How hardware choices impact large-scale training and inference
I created TensorRigs to bridge the gap between academic AI hardware research and practical purchasing decisions for researchers, developers, and enthusiasts working with deep learning workloads.
Connect & Collaborate
Have questions about AI hardware or need help optimizing your deep learning setup? I'm always interested in discussing hardware optimization challenges and sharing insights from my research.