January 2026 monthly summary focused on delivering CUDA Graph-based acceleration, broader graph-enabled execution support, and documentation improvements across PaddlePaddle and FastDeploy. The work emphasized business value through performance gains, scalable testing, and smoother deployment readiness for CUDA/XPU workloads.

November 2025 highlights for PaddlePaddle/FastDeploy: Enhanced XPU deployment capabilities with V1 model loader support for bf16 and weight-only loading across multiple quantization types, improving load performance and deployment flexibility. Expanded weight-only loader path to support additional quantization types including wint8. Published XPU release 2.3 documentation with installation guidance and deployment examples to accelerate user adoption. Demonstrated emphasis on code quality with style updates and collaborative commits.

October 2025: Delivered significant XPU-accelerated inference enhancements and stability improvements for PaddlePaddle/FastDeploy. Key feature work focused on W4A8 quantization support for XPU and encoder-decoder inference improvements, including refactoring of the block attention kernel, improved KV cache management, and enhanced inference parameter handling for encoder-decoder models. Implemented targeted fixes to improve robustness and test stability across MoE paths and preempted tasks.

Month 2025-09: Delivered stability and documentation enhancements for PaddlePaddle/FastDeploy. Key outcomes include fixing XPU stability and potential OOM when ENABLE_V1_KVCACHE_SCHEDULER is enabled, by adjusting max_num_batched_tokens to max_model_len and refining prefill/decode handling; and updating release notes and installation guidance to point to the latest FastDeploy and PaddlePaddle versions with explicit Docker image tags and package versions. These changes improve runtime reliability, simplify deployment, and accelerate customer adoption of the latest stack.

August 2025 Monthly Summary for PaddlePaddle/FastDeploy: Focused on stabilizing XPU deployment workflows and improving release documentation to support larger models and reliable memory management across single- and multi-XPU scenarios.

Summary for PaddlePaddle/FastDeploy (July 2025): Focused on delivering high-value features for large-model inference, stabilizing the codebase by removing obsolete components, and improving developer experience through clearer diagnostics and documentation. Delivered targeted XPU integration improvements, reduced maintenance surface, and enhanced benchmark tooling feedback to accelerate debugging and onboarding.