MLSYSIM: Modeling ML Infrastructure from First Principles

Machine learning has moved beyond being a laboratory experiment to become the backbone of critical infrastructure in companies across all sectors. However, designing systems that work from a low-power microcontroller to a multi-gigawatt datacenter cluster remains a huge challenge. Traditional tools —empirical profiling on real hardware or cycle-accurate simulations— are slow, costly, and inflexible for exploring architectural alternatives. In this context, MLSYSIM emerges, an analytical modeling framework based on first principles that formalizes what its creators call 'systems physics'. Its Python engine enforces strict dimensional analysis at runtime, avoiding the silent conversion errors that plague ad hoc models. The core abstraction separates computational demand (what the algorithm needs) from silicon supply (what the hardware provides) and the environmental context (power, cooling, latency). Each parameter is incorporated from a typed registry with source traceability, ensuring no number enters the analysis without a documented source. On top of this engine, MLSYSIM encodes a taxonomy of 22 'system walls' resolved through 28 composable models and solvers, allowing exploration of thousands of configurations in under a second. For a company developing custom applications with artificial intelligence components, having such a tool means being able to identify the exact bottleneck —whether memory, bandwidth, or compute— before investing in infrastructure. At Q2BSTUDIO, we understand that modern systems engineering demands convergence between custom software, artificial intelligence services for businesses, cybersecurity, and cloud platforms like AWS and Azure. That is why we integrate early modeling methodologies into our projects, from AI agents to Power BI dashboards, including process automation. The ability to simulate the behavior of a complete system before implementation reduces risks, accelerates time-to-market, and optimizes infrastructure spending. MLSYSIM represents a conceptual advancement that complements cloud services and business intelligence practices, offering a comprehensive view of performance. Ultimately, modeling from first principles is not just an academic curiosity: it is a strategic tool for any team building critical technology.