GPU Lab
Developed for
ML engineers, Labs contributors, and organizations planning GPU spend for Arabic AI workloads
The challenge
- Catalogs list FLOPs and VRAM without tying them to LLM fit
- Teams overbuy or underbuy GPUs for inference vs QLoRA
- Hard to communicate capacity tiers to non-specialists
The Solution
GPU Lab catalogs 100+ GPUs into performance tiers and estimates which LLM sizes fit for inference and fine-tuning, with OS detection helpers and capacity summaries.
Key capabilities include:
- ◇ Tiered GPU performance ladder (consumer → datacenter)
- ◇ LLM capacity estimates from VRAM and compute
- ◇ Public tool at /gpu-lab for hands-on exploration
Impact
Shortens hardware planning cycles and aligns Labs experiments with realistic GPU budgets.
Organization benefits:
- Clearer procurement conversations
- Fewer failed fine-tune attempts on undersized cards
- Shared reference for Labs onboarding
Tags
Research Team
Meet Our PIs
Discover the principal investigator behind this project and the expertise that made it possible.
Hamza Salem
Head of PYXON Labs
Leads PYXON Labs research across Arabic AI, edge systems, governance, and applied products that ship into real environments.
// Open Vacancies
Join as a Scientist
Join our team working on GPU Lab. Explore opportunities in machine learning, Arabic NLP, computer vision, edge systems, and applied AI research.
View Open PositionsApply to this project
Submit the same scientist application used on PYXON Labs — tell us about your CV and how you’d contribute to GPU Lab.
Or email info@pyxon.com
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