February 2026 monthly performance summary for tenstorrent/tt-llk. Focused on implementing a fast approximate exponential function (Schraudolph-based) to accelerate compute-intensive models, with robust validation. Delivered a parameterizable, well-tested approximation that reduces per-tile cycles and preserves accuracy within a defined input range, enabling faster inference in SDPA workflows and similar workloads.

December 2025 monthly summary for tenstorrent/tt-inference-server. Focused on simplifying device configuration to accelerate onboarding and reduce operational risk. Delivered a configuration simplification feature that removes unused parameters, improving setup speed and reducing potential misconfigurations. No major bugs fixed this month. Overall impact: faster deployment, lower support load, and a cleaner configuration surface. Skills demonstrated include code refactoring, configuration management, and commit-driven delivery.

November 2025 monthly summary for tenstorrent/tt-inference-server. Delivered a key feature to standardize Wan device video output by making 720p the default resolution on Galaxy, coupled with fixes to blackhole handling and a refactor of fabric configuration. The work improved reliability and performance of video processing in BH scenarios and simplified future maintenance through centralized fabric settings and small but impactful tweaks across the inference server.

October 2025 monthly summary for tt-inference-server focused on expanding model support, reliability, and documentation. Delivered Mochi and Wan model runners, enhanced server capabilities to support additional models, and aligned internal naming with modelrunners. Updated configuration and README to ensure proper setup and usage.