Infinity’s Agentic Tools Get AI Chips Inference-Ready in Days
Joint engineering effort compresses weeks of low-level kernel optimization into a rapid iteration cycle, using autonomous tooling to map Qwen3 onto d-Matrix's SRAM-based Corsair architecture
Infinity (Infinity Artificial Intelligence Institute), an early-stage AI infrastructure research company building the software layer that makes any AI chip inference-ready, today announced a new case study that showcases tools for its autonomous research and tool building agent, Ignition, which generates, test and optimize the low-level compute kernels, compilers, profilers, debuggers and SDKs that determine how efficiently a chip runs AI models. Developed for a design partnership with d-Matrix for its SRAM-based inference accelerator Corsair, Infinity’s new AI product drastically reduces the time chip companies need before chips are ready for mass market adoption. Ignition is a concrete example of ongoing recursive self-improvement (RSI), as an AI system that builds and autonomously researches the training and inference layers for the next generation of AI systems.
New AI chips are frequently held back not by their hardware capabilities, but by the absence of a mature software stack, precisely what NVIDIA has spent the past two decades building around CUDA. That gap is what typically keeps promising accelerators out of production, and inference now accounts for a growing majority of AI compute spending industry-wide. Infinity’s new tools autonomously iterate on hardware representation and kernel design without requiring a large team of specialized kernel engineers and years of work for each new chip.
