March 2026 monthly performance summary for vLLM-Ascend: Delivered two major features aimed at stabilizing and accelerating inference under dynamic workloads, with real-time telemetry-informed load balancing and unified MoE placement. These changes improve cross-device load balance, reduce redeployment overhead, and boost throughput, demonstrating strong proficiency in distributed ML systems, MoE architectures, and performance engineering.

During January 2026, focused on stabilizing distributed MoE deployments on the Ascend platform in vLLM-Ascend. Delivered two critical bug fixes that remove runtime shape errors and numerical inaccuracies, enabling reliable routing and load balancing for large-scale MoE models. Specifically, addressed a shape mismatch between expert_placement_map and log2phy_expert_map when redundant experts are enabled, aligning shapes during initialization and EPLB adjustments, and added assertions to prevent silent errors. Also fixed a moe_load accumulation bug in ACL graph mode on NPU by replacing in-place += with add_(), ensuring correct accumulation. Implemented shape consistency checks post-initialization and EPLB updates to proactively catch misalignments. These changes preserve compatibility with non-redundant deployments and align with vLLM release v0.13.0, delivering business value through increasing stability, correctness, and scalability of MoE routing and load balancing.

Concise monthly summary for 2025-12 focused on delivering modular, scalable improvements and stabilizing core algorithms across two repos. Key results include a major EPLB policy refactor to improve modularity and performance, and a reliability fix for the FlashLB warm-up invocation to prevent runtime errors during pre-compilation. The work enhances distributed load balancing, reduces risk of runtime failures, and demonstrates strong collaboration and code hygiene.

Month: 2025-10 — Delivered a new EPLB Algorithm Configuration Interface in the rjg-lyh/vllm-ascend repository, enabling end users to select and tailor the EPLB algorithm. This improves usability, accelerates experimentation, and preserves internal stability by exposing a clear configuration surface. A linked bugfix exposed the user policy type interface to support policy-driven configurations, ensuring a stable and predictable configuration surface. Overall, the work enhances configurability, reduces setup time for experiments, and strengthens maintainability across the codebase.

September 2025 — Delivered FlashLB joint-optimization for EPLB replica allocation and placement in rjg-lyh/vllm-ascend. Implemented the FlashLB algorithm enabling joint optimization, multi-shot enhancement, and incremental adjustment to reduce per-device hotness and adapt to time-variant expert hotness compared to the default EPLB. This work improves scalability, latency stability, and resource utilization for EPLB deployments and lays the groundwork for ongoing performance tuning and monitoring.