Systalyze

Last week, Sarah Laszlo, PhD and her team at Visa shared remarkable results from their implementation of Systalyze's ML infrastructure platform. These powerful insights from our partner at Visa highlight the unique challenges enterprises face when building proprietary AI: there's no roadmap for optimizing models on your unique data. When training AI models on transaction data—where hyperparameter spaces remain unexplored and optimization is typically done manually—the costs add up quickly: • Wasted GPU cycles  • Inefficient resource allocation  • Engineering time spent exploring hyperparameter spaces  • Suboptimal model performance Systalyze’s platform removes these bottlenecks for enterprise deployments at scale. With the massive growth of AI and agentic workflows, we are transforming how enterprises deploy and optimize their entire AI/ML pipeline. By integrating our optimization technology into Visa’s AI pipeline, we achieved: • 8x faster training in Kubernetes environments  • 8x performance improvement over Ray  • 7.4x acceleration compared to Run:AI These aren't just technical metrics—they represent $10s of millions in cost savings while enabling Visa to build sophisticated AI models that were previously impractical. Want to see what Systalyze can do for your ML workloads? Sign up for our free AI cost checkup at https://systalyze.com/ #AIOptimization #AIInnovation #ScalableAI #CostEffectiveAI #AIForEveryone #FinTechAI

AI is transforming industries, but as enterprises shift to private AI for security, performance, and compliance, new challenges around cost and efficiency arise. That’s why we’re so excited to partner with Bayer’s AI Innovation team—leaders in healthcare AI. Together, we’re tackling these optimization challenges to help businesses scale AI sustainably. Sign up for a free AI cost checkup at https://systalyze.com/. Huge thanks to the Bayer team for making this possible—what a ride it’s been, and the fun’s just getting started! Luis Muniz, Ph.D., Steffen Vogler, Sadegh Mohammadi - CAPM®, Pedro L. C. Osório, Guillermo Jimenez-Perez, Javier Montalt Tordera, Markus Blank

Earlier this month, DeepSeek shook up the world with DeepSeek-R1, a model that enables complex LLMs to run seamlessly on low-end GPUs. This innovation challenges industry giants, disrupting the belief that advanced LLMs require massive computational resources. At the core of DeepSeek-R1 is its ability to enhance reasoning capabilities with synthetic data generation, where the model learns from its own high-quality outputs while employing architectural and low-level code optimizations to maximize efficiency. A key factor behind this breakthrough lies in its strategic use of GPU resources, employing hardware-aware scheduling, optimized memory management, and computation-efficient design to enable high-performance AI on lower-end hardware. This advancement not only makes AI more scalable but also significantly reduces operational costs.   These fundamental principles behind DeepSeek’s efficiency gains—hardware-aware optimizations, fine-tuned memory handling, and adaptive training methodologies—strongly resonate with Systalyze’s offerings. While DeepSeek focuses on model training efficiency, Systalyze extends these principles to real-world enterprise fine-tuning, inference deployments, and agentic application development. This is a crucial step toward making AI more efficient, cost-effective, and sustainable for everyone. Stay tuned as we continue pushing the boundaries of AI optimization and deployment!  #AIOptimization #AIInnovation #ScalableAI #CostEffectiveAI #AIForEveryone