The "FlagOS Open Computing Global Challenge" is a multi-season competition jointly hosted by FlagOS Community, Beijing Academy of Artificial Intelligence (BAAI) and CCF Open Source Development Technology Committee (ODTC). By encouraging developers to engage in hands-on creation and innovative exploration based on the unified AI system software stack provided by FlagOS, the competion aims to enhance the skills of AI developers.
With a total prize pool of 2 million RMB, winners will share the rewards and receive official honorary certificates from the organizers. We cordially invite AI algorithm and system engineers, hardware and compiler developers, as well as all technical practitioners and visionaries dedicated to overcoming the complexities of AI chip programming to join this challenge and collectively advance the thriving development of open computing ecosystem.
Season 1 will open for registration on January 9, 2026, with final results to be announced in early June 2026. This season focuses on three core tracks: Operator Development and Optimization, LLM Inference Optimization, and Automatic Data Annotation. The goal is to deeply optimize the performance and operational efficiency of large language models while driving technical implementation and industrial innovation.


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## S1 Challenge Overview

This season features three distinct tracks, each targeting specific dimensions of development expertise:
- Track 1: Focuses on low-level operator implementation and cross-hardware deep optimization capabilities.
  - https://www.kaggle.com/competitions/track1-operator-development-and-optimization-flagos-challenge/overview
- Track 2: Focuses on the maximal excavation of full-stack inference performance and system-level tuning expertise.
  - https://www.kaggle.com/competitions/track-2-large-model-inference-optimization
- Track 3: Focuses on the ability to perform automated data annotation within long-context scenarios.
  - https://www.kaggle.com/competitions/track-3-llm-automatic-data-annotation-in-long-context-scenarios