June 2026: Delivered Shared Expert Fusion for Qwen3.5 on ROCm and established ROCm-friendly fusion flow. Implemented compatibility checks to ensure shared and routed experts use the same quantization format before fusion and remapped shared expert weights to the fused expert slot when fusion is enabled. This work, anchored by a ROCm-focused fix, improves deployment readiness and sets the stage for faster, more memory-efficient inference on ROCm platforms.

Month: 2026-05 — Performance-focused delivery in jeejeelee/vllm with Mixture of Experts (MoE) routing enhancements and configurable dispatch policy to optimize throughput and efficiency. The work emphasizes business value by reducing routing overhead, enabling tunable performance per workload, and laying groundwork for more scalable MoE deployments.

April 2026 focused on correctness in the ROCm/aiter fused MOE path. Implemented a critical bug fix by correcting the moe_sorting_dispatch_policy annotation from bool to int in the PyTorch custom op schema, ensuring dispatch_policy values (e.g., 2) are preserved rather than coerced to True. The change is anchored by commit a4e989071bc53b610379f0a62433f8e443f627a0 for issue #2639 and resolves #2576, improving policy accuracy and preventing silent misbehavior in production. Impact: improved correctness and reliability of the fused MOE dispatch mechanism, reducing risk in production workloads. This work also strengthens type-safety in PyTorch custom op schemas and demonstrates disciplined code hygiene and traceability.