March 2026 (vllm-ascend): Key stabilization, performance optimizations, and developer workflow improvements focused on business value and reliability. Key features delivered: - Extended Sequence Parallelism (SP) support for VL MoE models and removed sp_threshold in favor of sp_min_token_num, enabling faster, more scalable inference. Triton-Ascend kernels added to penalties to reduce sampling latency, with measurable gains in end-to-end latency. Major bugs fixed: - Bug fix: Restored enable_sp-based branching to fix accuracy issues introduced by replacing it with enable_flash_comm_v1; ensured consistent behavior when enable_shared_expert_dp is enabled. Validated with server startup and curl tests; no user-facing changes. Overall impact and accomplishments: - Per-request throughput improved for VL MoE workloads (TTFT reductions observed: 4k seq from ~429.4 ms to ~323.3 ms; 16k seq from ~1297.0 ms to ~911.7 ms). These changes increase model throughput and reduce latency, enabling better user experience for chat and reasoning workloads. - NPUWorker Profiler adapted for API parity with upstream vLLM, including lazy initialization and per-worker unique trace files, facilitating more accurate profiling and easier multi-worker debugging. - Developer experience improved via AGENTS.md updates, clarifying sign-off requirements, PR title formats, and lint steps, reducing onboarding friction and raising code quality. Technologies/skills demonstrated: - Python/config changes for SP and VL MoE, performance benchmarking, and unit/integration testing. - Triton-Ascend kernel development for penalties and performance tuning. - Profiler adaptation, API parity work, and profiling trace management for multi-worker environments. - Documentation and governance improvements to contributor guidelines.

February 2026 performance summary for vllm-project/vllm-ascend. Key feature delivered: VL Model Inference Sequence Parallelism, designed to boost inference throughput by optimizing communication patterns in VL models. The work includes configurable options and validation tests to ensure correctness under specified conditions. This lays the groundwork for higher throughput on latency-sensitive VL workloads and provides measurable performance gains when enabled. Link to delivery: commit 5def28dcd3f6330e583671f0880b3452151ef10a ([Feat]support sequence parallelism by pass for VL models (#5632)).

December 2025 performance and technical achievements across vllm-ascend and vLLM projects. Delivered GPU memory management optimization, reworked sampling pipeline for improved accuracy, stabilized main branch ahead of release, and fixed critical spec decoding edge cases. Demonstrated strong cross-repo collaboration, rigorous testing, and release readiness.

Monthly summary for 2025-11 (vllm-ascend): Focused on performance optimization for large-sequence inference and robust fixes to quantization handling and async scheduling. Delivered measurable throughput improvements and stability enhancements across the vLLM Ascend integration, enabling more reliable, scalable deployments and improved user-facing performance.

October 2025 (vllm-ascend) focused on boosting distributed performance on A2 hardware, improving model runner latency for small-parameter models, and stabilizing flash communication. Delivered features enhance distributed training/inference throughput and reduce idle time, while fix-packages improve logging, data handling, and robustness in flash communication. Key business impact: higher throughput, lower latency for end users, improved reliability in distributed setups, and clearer operational logging for troubleshooting.

2025-09 Monthly Summary for vllm-ascend: Focused on stability and reliability improvements for non-TP configurations. Delivered a critical bug fix in DenseOptimRowParallelOp when tensor parallelism is disabled (tp=1), ensuring the correct layer argument is passed to quant_method.apply in SequenceRowParallelOp. This restoration of correct operation eliminates instability in non-TP mode and reduces runtime risk for production deployments. The change is compatible with both vLLM v0.10.2 and the main branch, with no user-facing changes. This work contributes to higher reliability in inference workloads and smoother customer deployments.