Inworld and Qwerky Deploy Mojo for Custom GPU Kernel Development

Modular AI partnered with Inworld and Qwerky AI to deploy custom Mojo GPU kernels for AI inference optimization. For Inworld, a company building AI products for consumer applications including realistic voice AI, Modular deployed its MAX Framework and Mojo language to create a state-of-the-art speech synthesis pipeline on NVIDIA Blackwell GPU Architecture. The collaboration achieved 70% faster time to first audio and 60% lower cost compared to vanilla vLLM-based implementations, returning the first 2 seconds of synthesized audio in just 200ms. The MAX streaming-aware scheduler minimized time-to-first-token for the Speech-Language Model component while custom Mojo kernels, including a tailored silence-detection kernel running directly on the GPU, enabled highly efficient inference. For Qwerky AI, Modular enabled the deployment of custom Mamba-based models across NVIDIA and AMD GPUs without rewriting native code for each platform. Qwerky engineers wrote a custom selective scan operation in Mojo in just 20-30 lines of readable code that automatically optimizes for both NVIDIA tensor cores and AMD matrix engines, achieving 50% faster GPU kernels and 3x research velocity. The unified MAX compilation pipeline allows Qwerky to iterate on model improvements in hours instead of weeks.