March 2026: Delivered Sparse Attention Backend with XPU optimizations for DeepSeek v3.2 in jeejeelee/vllm. Implemented new sparse data operations and integrated with existing attention mechanisms to boost throughput for sparse workloads. The work is documented in commit e584dce52b9584ffb0fc4a1a4cd31163d4257a41, which includes signed-off by Zhang, Wuxun (intel). No major bugs fixed this month for this repo; stabilization and validation efforts focused on performance and reliability of the new backend.

Monthly work summary for 2025-12 focusing on kernel correctness improvements in intel/sycl-tla. Delivered a targeted FMHA forward kernel output shape fix for variable-length inputs with a single KV head, preventing incorrect computations and improving model reliability in production workloads. The fix is backed by a patch (commit 2c7282d5f269aa883608afb77540e9d975d3879e) and Xe20-based validation.

November 2025 monthly summary focusing on key accomplishments and business impact. Delivered a critical bug fix in vllm-gaudi that updates finished KV transfer state after decoding forward runs, reducing TTFT and improving state management in P/D disaggregation. Also introduced a persistent SDPA kernel in intel/sycl-tla to balance workloads across XeCores for decoding workloads, improving throughput and resource utilization. Both efforts demonstrate strong cross-repo collaboration and hands-on performance optimization.

October 2025: Delivered scalable DP-enabled distributed inference enhancements in vllm-gaudi, with DP padding handling improvements, padding-aware max-tokens calculation, and unified attention across DP groups to improve correctness and throughput in multi-rank configurations. Implemented distributed inference orchestration improvements to optimize model-parallel KV scheduling and DP disaggregation, including optimized dummy prefill runs and ensuring proper ModelRunnerOutput state during async scheduling. Addressed stability and performance with upstream DP padding fixes, and memory efficiency gains by reusing DP allgather tensors across layers when HPU graph is enabled. These changes collectively increase multi-rank throughput, reduce idle time, and lower memory footprint, enabling more scalable deployments with no loss in accuracy.

September 2025 monthly summary highlighting distributed Gaudi-based inference work, DP stability improvements, and MOE sequence-parallel enhancements across Gaudi deployments. Focused on delivering business value through scalable, reliable inference for large language models and improved CI validation.