February 2026: Delivered Wan2.2 Tensor Parallelism (TP) support for Wan2.2 model in vllm-omni. This work enables scalable distributed deployments, enhances throughput, and supports larger model configurations across multi-GPU environments. Key commit: c4933ec2aa930400d5ac32a6b037b74e5cd2a56e. Focused on TP size arguments, feed-forward network adjustments, and distributed normalization techniques. This accelerates model serving and training in distributed setups, reducing per-inference latency and increasing capacity.

December 2025 monthly summary for vllm-ascend: Stabilized and simplified the MoE path on Ascend while removing legacy dependencies. Delivered key features that improve reliability, maintainability, and readiness for future MoE enhancements, aligned with the vLLM 0.12.0 baseline. Achievements include backend stability improvements, refactored reduction logic, and a cleanup of deprecated components, all validated with end-to-end and unit tests.

November 2025 (vllm-ascend): Focused on performance optimization and stability improvements to enhance throughput, scalability, and reliability of MoE workloads, with no user-facing changes. Key work shipped in two commits/pull requests and aligned with CI migration plans.

Month: 2025-10. This period focused on strengthening the Mixture-of-Experts (MoE) backend for vLLM Ascend deployments by improving architecture, stability, and test coverage, while keeping behavior consistent for end users. The work achieved a cleaner MoE codebase, reduced production risk, and prepared the path for scalable inference on large models.