May 2026 focused on delivering business-value features in PaddlePaddle/FastDeploy by strengthening the Execution Pipeline and broadening hardware compatibility. Key work included a batched token scheduler for the EP component to improve throughput and stability (reverting TTFT optimizations), plus CUDA SM103 support to optimize for newer NVIDIA hardware. These changes reduce latency under high-concurrency workloads, expand deployment scenarios, and demonstrate solid CUDA handling and performance tuning.

Month: 2026-04 — Delivered key features, bug fixes, and performance improvements in PaddlePaddle/FastDeploy. Focused on MoE top-k reduction, GLM rotary embedding optimization, dependency cleanup, bias-free deepgemm support, and FP8 quantization inference reliability. These efforts deliver improved model selection, faster embedding computations, reduced dependency surface, greater flexibility for GEMM paths, and corrected FP8 inference across SM configurations.

March 2026: Delivered a key feature in PaddlePaddle/FastDeploy—Qkrmsnorm support in the normalization layer via paddle.nn.functional.rms_norm. This adds a more flexible and efficient normalization path and removes unnecessary flags to simplify the codebase. The work aligns with the proxy-norm approach for qkrmsnorm and is prepared for upstream review. Commit: f95d8ca7df8b0810d58a04d06fc4f27e7e8e40d1.

February 2026 monthly summary: Focused on delivering high-impact features for PaddlePaddle/FastDeploy, including Ernie FP8 Quantization on SM100 and DeepGEMM integration, with refactoring to support new quantization methods and standardized import paths. Expanded testing and alignment with fleet operations to boost reliability, performance, and maintainability.

2026-01 monthly summary for PaddlePaddle/FastDeploy: Implemented Ernie FP8 support on SM100 with block-wise FP8 inference and DeepGEMM optimizations, delivering notable performance and accuracy improvements. Extended device compatibility to 21B-tp2 and dev_paddle, validated on single-machine 4.5T EP configurations. Due to refinement needs, the feature was temporarily reverted to ensure stability, with a plan to reintroduce after addressing edge cases. The work demonstrates strong capabilities in FP8 paths, optimized compute kernels, and cross-team collaboration, setting the stage for a more robust Ernie FP8 path in subsequent releases.

Month 2025-11 — PaddlePaddle/FastDeploy: Command Usage Simplification. Delivered a documentation-focused change that aligns CLI usage with the actual defaults by removing the --load-choices "default_v1" parameter from user-facing docs, streamlining command usage and reducing user confusion. No major bugs fixed this month; primary delivery was a feature-oriented documentation cleanup tied to a single commit. This change improves user onboarding, lowers support load, and establishes a clearer baseline for default behavior, benefiting both users and maintainers.

Month 2025-10 — PaddlePaddle/FastDeploy: Improved platform compatibility and stability by delivering a targeted bug fix that enables graceful handling of image operation imports on unsupported platforms, expanding hardware support and reducing runtime failures. The change stabilizes image-related operations across diverse environments by updating ForwardMeta inheritance (HPUForwardMeta to inherit from ForwardMeta) and converting hard ImportErrors into warnings in image_op.py, enabling safe fallback paths and easier future extension.

Month: 2025-09 — PaddlePaddle/FastDeploy: Focused on improving ERNIE onboarding and documentation to accelerate adoption and deployment reliability. Delivered enhanced guidance, updated defaults and environment/setup docs, and introduced a load optimization flag to speed ERNIE loads with better memory efficiency. No major bug fixes recorded this period. Impact: faster onboarding, clearer deployment paths, and improved ERNIE runtime performance; supports faster time-to-value for data scientists and engineers.

Concise monthly summary for PaddlePaddle/FastDeploy (2025-08): delivered robustness improvements to stop sequence handling in the LLM engine with accompanying unit-test fixes, and consolidated ERNIE deployment/docs best-practice updates. These efforts improved reliability, clarity, and developer/user efficiency, contributing to reduced runtime errors and better guidance for configuration and usage.

July 2025 performance focused on extending generation controls, improving deployment readiness, and enhancing developer experience for PaddlePaddle/FastDeploy. Delivered a key feature for custom stop sequences in multi-end generation, expanded early stopping capabilities via new documentation, and published ERNIE-4.5 deployment guidelines. These efforts improved output control, serving reliability for online/offline inference, and clarified deployment best practices.

February 2025 monthly summary for PaddlePaddle/Paddle highlights the delivery of targeted test coverage for distributed LLM inference on the Llama model, strengthening validation of distributed execution paths and reducing release risk. This work focuses on test-driven validation, build integration, and maintainable test infrastructure to support scalable AI workloads. Impact: By validating distributed inference early, the team accelerates release readiness and provides measurable confidence in Paddle's ability to scale LLM workloads on distributed hardware.

Month: 2024-11 — PaddlePaddle/Paddle Key features delivered: - Paddle Inference: Remove fleet executor functionality from AnalysisPredictor, removing fleet executor code, related configurations, and dependencies to simplify the inference API and remove deprecated distributed model inference features. Major bugs fixed: - No major bugs fixed in this scope for PaddlePaddle/Paddle during 2024-11. Overall impact and accomplishments: - API simplification reduces maintenance burden and risk, improves developer onboarding, and sets the stage for future inference API improvements. Primary delivery captured in commit 64c7181e725fe80ba5c89614b475e5d232d051fc with message "[Inference] Remove fleetexe in Predictor (#69710)". Technologies/skills demonstrated: - Inference API design and refactoring - Code cleanup and dependency pruning - Git-based patch delivery and change traceability Business value: - Reduced surface area for distributed inference, lowering risk and enabling faster iteration of future inference API changes, with clearer API semantics for AnalysisPredictor.