The Beijing Academy of Artificial Intelligence (BAAI) has unveiled FlagOS 2.2—the latest version of its open software stack designed to run artificial intelligence models across various chip architectures. This release was made on September 30th and demonstrated at the AI Open Computing Conference in Beijing on October 17, 2026. BAAI characterizes this update as a shift from adapting models to multiple chips to optimizing the general code for them.
The main achievement is the scale. FlagOS 2.2 covers nine operator libraries, and the number of public operators in FlagGems and related libraries has increased from 564 to 1454, an increase of 890 units. The new FlagGems-SGLang library added 40 multi-chip operators that cover functions such as attention, Mamba state updates, expert mixing, LoRA, sampling, and quantization. BAAI asserts that thanks to over a thousand operators across 10 types of AI hardware, overall operator performance has increased by approximately 27%.
A significant part of this growth is due to automation. BAAI's KernelGen operator generator now supports nine types of AI chips and created 765 new operators this quarter. Of these, 416 passed accuracy checks on at least five domestic chips, achieving over 80% of native operator performance. Furthermore, the unified debugging and profiling tool FlagPrism connects Triton source code data, compiler intermediate representation, and on-device execution data, supporting Huawei Ascend, Iluvatar CoreX, Moore Threads, Nvidia, and Enflame.
The release also includes FlagOS-Compressor for weight conversion and quantization between chips. BAAI reported that applying W8A8 to the Qwen3.5-397B-A17B model reduced the model file size from 751.41 GB to 382.45 GB, a decrease of 49.1%, while the model achieved 84.85% on GPQA-Diamond. Combined with framework plugins for vLLM, Megatron-LM, SGLang, and others, new models can be adapted for up to 12 chip platforms within just one day of release.
Research using older hardware demonstrates success. On Ascend 910B servers, BAAI noted that the logical serving throughput of the GLM-5.3-Flash model in agent reproduction tests increased 2.04 times compared to pre-optimization levels, rising from 8.52k to 17.36k tokens per second. The Xing4.0-29B-A4B model from China Telecom was adapted for 10 chip platforms, resulting in a 19.87% reduction in time-to-first-token in long-input tests compared to native operators.
BAAI believes the issue lies in timelines: over 80% of deployed AI computations relate to non-latest generation, while models are rapidly changing. It should be noted that all figures presented are based on BAAI's internal tests, and the company emphasizes that actual serving performance still depends on the workload.
