May 2026 monthly summary focused on delivering NPU-accelerated MiniMax-M2.7 support in jd-opensource/xllm, with optimized attention and dequantization to improve loading and inference performance. No major bugs fixed this period; groundwork laid for future NPU-enabled models and broader model support.

March 2026 (2026-03) focused on delivering a high-value inference capability for large models on NPU devices within the jd-opensource/xllm repository. The work enhances deployment flexibility, performance, and resource efficiency for production-scale AI tasks, with clear documentation to accelerate adoption across teams.

February 2026 (Month: 2026-02) - Summary of developer work for jd-opensource/xllm. Delivered two critical items: a bug fix addressing runtime errors for multi-machine MTP configurations and a feature enabling index cache transfer in the PD disaggregation workflow. The changes improved cross-machine reliability, reduced runtime errors, and introduced an indexing mechanism to accelerate data retrieval and storage across multiple layers, particularly benefiting lighting indexers and large-language-model performance.

Concise monthly summary for 2026-01 focusing on jd-opensource/xllm: delivering business value through hardware-accelerated multimodal capabilities and strengthening deployment readiness on NPU devices.

May 2025 monthly summary for vllm-ascend: Delivered Large Model Support via NPU Memory Optimization to enable 32K model lengths and address Out of Memory errors. Implemented memory-efficient in-place multiplication to maximize throughput and support longer sequences with the existing NPU. Focused changes align with DeepSeek r1 W8A8 configuration. Overall, these improvements reduced memory pressure, increased model capacity, and improved reliability for large-model deployments.