January 2026 monthly summary: Focused on accelerating distributed decoding in PaddlePaddle/FastDeploy. Implemented distributed communication enhancements by adding support for communication groups in custom all-reduce and delivering a fused all-to-all/transpose operator, significantly improving decoding efficiency and scalability. These changes enable higher throughput for distributed inference and lay groundwork for broader deployment.

December 2025: Delivered substantial performance and scalability improvements in PaddlePaddle/FastDeploy. Implemented Multi-Query Attention scalability with split-KV mechanisms and GPU memory optimizations to boost throughput for long-sequence models, and completed throughput-oriented model execution optimizations across tensor/embedding parallelism, MOE forward path, and prefill handling. These changes improve production inference throughput and stability for large-scale deployments, with collaborative fixes and environment-driven configuration.

Month: 2025-11 — Delivered focused improvements in PaddlePaddle/FastDeploy across distributed inference and GPU optimization. Key outcomes include: 1) Inter-node two-stage parallel processing support (internode_ll_two_stage) with configuration updates, argument parsing, and engine logic to enable distributed two-stage processing and improved cross-node data handling (commit af7e0f27f3706757dfd89c6292cc830a365d08c9). 2) GPU dynamic scaling optimization for multi-query attention, refactoring GPU operations to support dynamic scaling for better performance and memory efficiency (commit 6c3d1da62f1fef75010374967d4b757c6e6c52af). 3) Rank calculation fix for parallel model executor, using local_data_parallel_id instead of expert_parallel_rank to improve correctness and parallel processing behavior (commit 3e9dda39abecc381046faaf5b821064aed61934e). Overall impact: increased scalability, throughput, and reliability for large-scale distributed inference; improved GPU utilization and memory efficiency; code quality improvements in configuration, parsing, and engine logic—delivering tangible business value for deployments.

Month: 2025-10 — Key feature delivered in PaddlePaddle/FastDeploy: Dynamic FP8 Quantization Support in the Speculative Decoding Cache. Implemented a new FP8 kernel and associated logic to enable FP8 data types in the speculative decoding cache, enabling more efficient storage and processing of key-value caches. RoPE (Rotary Positional Embedding) and RMS normalization were integrated within the FP8 path to improve performance and accuracy. The work reduces memory footprint and increases inference throughput, supporting cheaper, scalable deployment of models with maintained accuracy. Commit 3aa04fbf214a5c1a8ac088cd4635fe3c0939b656 includes the change; co-authored by freeliuzc.

September 2025 (PaddlePaddle/Paddle): Focused on stability and performance improvements in the Deep EP path through robust buffer lifecycle management for low-latency two-stage inference. Delivered a dedicated buffer cleanup mechanism and enabled clear_buffer support in the mixed_infer flow to prevent stale/bad buffers across internode two-stage inference runs.

Monthly summary for 2025-08 focusing on PaddlePaddle/Paddle feature delivery and impact.

July 2025 PaddlePaddle/Paddle – Monthly Summary Focus: business value, reliability, and distributed inference performance with a tight emphasis on correctness and scalability.

May 2025: Delivered reliability improvements and performance optimizations for PaddlePaddle/Paddle. Key outcomes include a memory-efficient attention compilation fix for architectures > sm90, Flash Attention v3 VarLen API support, and NVLink-based internode optimization for deep_ep. These changes broaden hardware compatibility, enable variable-length sequence processing in attention, and improve distributed training throughput.

2025-03 PaddleNLP momentum centered on elevating MLA (Multi-Layer Attention) robustness and performance through block-size flexibility and low-precision accumulation. This work enables attention computations to adapt to varying sequence lengths while offering a faster path via WG4 low-precision accumulation, aligning with efficiency and scalability goals.