Month: 2026-05 | Repository: aobolensk/openvino | Focus: MOE (Mixture of Experts) path improvements delivering correctness and performance for small-batch processing. Key features delivered and bugs fixed: - MOE Prefill Regression Fix: Addressed a regression in MOE prefill by ensuring necessary variables are stored and not reset, preventing fallback to a slower processing path. (Commit: 5ec01181735c55a23fbaa9242327890949825076) - MOE Batched GEMV Optimization for Small Batch Processing: Implemented a batched GEMV path to optimize MOE inference for small token counts, yielding approximately 20% faster performance during speculative decoding. (Commit: e01939759b2a2c434c8198f0f2831771670a3c6d) Impact and accomplishments: - Improved reliability and latency for MOE-based decoding with small tokens, reducing time-to-result and resource utilization on GPU. - Changes are tied to CVS tickets CVS-186728 and CVS-186747, with unit tests and performance measurements validating the improvements. - Clear separation of fixes and optimization paths, facilitating maintainability and future enhancements. Technologies/skills demonstrated: - GPU-oriented optimization and kernel path adjustments - Batched GEMV algorithm development and tuning for small batches - Performance benchmarking, unit testing, and traceability to work-items - Cross-functional collaboration evidenced by ticket alignment and commit traceability

April 2026 monthly summary focusing on business value and technical achievements across two OpenVINO repositories. The team delivered GPU-accelerated MoE enhancements, memory and I/O optimizations, and stronger default performance paths for large-scale models, with tests validating correctness across multiple model families. Key features delivered: - aobolensk/openvino: GPU optimization for moe paths (symmetric quantization for moe_3gemm) and a new GPU pass for shared expert fusion, with cross-model validation across GPT/Qwen families. - openvinotoolkit/openvino: MoE grouped GEMM optimization integrated by default to moe_3gemm, with an opt-out via MOE_USE_GROUPED_GEMM_PREFILL=0, improving GPU GEMM performance for MoE workloads. - openvinotoolkit/openvino: GPU model cache parallel I/O to boost large-cache reads from NVMe devices using a parallel I/O approach. Major bugs fixed: - aobolensk/openvino: GPU memory allocation bug fix for qwen3 moe models on dGPU, ensuring proper USM device allocation and robust buffer handling to sustain performance. Overall impact and accomplishments: - Substantial uplift in MoE inference throughput and latency, enabling faster large-model experimentation and deployment. - Improved cache loading times and model readiness on GPU workflows, reducing startup/latency bottlenecks for production workloads. - Strengthened cross-repo collaboration with clear ownership and test coverage across diverse models. Technologies/skills demonstrated: - GPU optimization, symmetric quantization, and fused-pass design for shared experts. - MoE optimization via grouped GEMM with safe default enablement and environment-driven configurability. - Parallel I/O patterns and memory management for GPU caches. - End-to-end validation across multiple LLM families and configurations; test coverage for edge cases. - Collaboration and cross-repo code review, with contributions and co-authorship where applicable.

March 2026 performance summary for aobolensk/openvino focused on GPU MOE optimization, reliability, and validation. Delivered a discrete GPU moe prefill regression fix to restore throughput on affected dGPU configurations, and introduced fused shared expert computation for sparse experts to reduce MOE kernel and host overhead. Expanded automated validation across multiple models (gtp_oss, qwen3_30b_a3b, LFM2-24B-A2B-Preview-TransformersV4, qwen3_next) to ensure reliability and business value. These changes enhance MOE scalability, boost inference performance, and demonstrate strong proficiency in GPU/heterogeneous compute, performance optimization, and test automation.

January 2026 – OpenVINO repo monthly summary focused on GPU-accelerated MOE/Qwen3 optimizations and kernel stability. Key delivered features include int8 weights compression for Qwen3 MOE on the oneDNN path with unit tests for u4 and u8, and silu_mul post-processing for micro_gemm to accelerate qwen3_moe. A MOE kernel build stability fix corrected argument-count mismatches to ensure qwen3_moe builds succeed. Commits contributing to these changes include 5ab80acea3ee87d367fcd49c4d65ff9a3b8f4cdb, 0ffa0defc715b0d3b5c5a12fa4db6ad3c9df5766, and 368a94e2c5c5b4f5a138767e02b51df7a34d188a. These efforts improve GPU performance on the onednn path, enhance test coverage and CI alignment, and reduce production risk in qwen3_moe deployments, with co-authored contributions from team members (CVS-178051; CVS-179195).

December 2025 performance month: Delivered GPU-accelerated prefill optimization for qwen3 in openvino by introducing micro_gemm-based parallelization, enabling parallel execution of experts during prefill and boosting throughput. Resolved a random accuracy issue for batch sizes greater than 1 and optimized the second-token latency for multi-batch runs. The changes were implemented in the openvinotoolkit/openvino repository, demonstrating improved throughput, stability, and scalability for high-throughput inference workloads. Business value is increased request throughput, better GPU utilization, reduced per-inference cost, and more reliable multi-batch performance across deployments.

November 2025 monthly summary for openvinotoolkit/openvino focusing on MOE (mixture-of-experts) performance and correctness improvements. Delivered a high-impact Qwen3 MOE optimization path with fused compression and flexible group size support, enabling scalable inference for large Qwen3 configurations. Implemented MOE3GemmFusedCompressed with fused softmax and one-hot operations, added a moe_3gemm pattern pass, and established a default group size of -1 for qwen3-30b-a3b. The work includes optimized prefill and decode stages leveraging GEMM kernels and OpenCL, respectively, to boost throughput and resource utilization. Also addressed a data type handling bug in MOE routing weights conversion to improve correctness and performance across GPU backends.

September 2025: Implemented a targeted fix for the Paged Attention primitive SHAPE_CHANGED handling in OpenVINO's OpenCL v2 path to ensure correct global/work sizes and computation accuracy, even when input shapes do not change; this stabilization improves model inference reliability in GPU-accelerated workloads and notebooks.

August 2025 (repo: aobolensk/openvino) delivered a focused set of GPU-attention enhancements and stability fixes. Key feature: OpenCL v2 infrastructure migration for attention, migrating PA and SDPA to a unified OpenCL v2 backend, refactoring kernels, updating registration, and paving the way for performance and maintainability gains. Major bugs fixed across GPU kernels included codegen macro detection robustness, SDPA optimization on A770, macro register and micro-kernel block size issues, transpose order, fmax datatype handling on Metal, and PA prefill buffer allocation. These changes improved correctness, stability, and memory efficiency, reducing production risk and enabling more consistent performance across Linux and Metal runtimes. Technologies demonstrated: OpenCL v2 kernel migration, GPU kernel development, codegen scripting, cross-hardware testing for A770 and Metal (MTL). Business value: improved throughput of GPU-attention workloads, reduced time to ship fixes, and stronger backbone for future performance/features.

April 2025 monthly summary for aobolensk/openvino: Delivered a GEMV kernel optimization for clDNN to accelerate second-token processing in Large Language Models (LLMs) for single-batch inputs. Introduced support for weight data compression types i4 and u4 with specific weight data layouts, enabling more efficient INT4 models. Demonstrated notable performance improvements for INT4 LLM workloads and contributed a key POC commit to the repository.

January 2025 focused on stabilizing GPU property handling in OpenVINO to prevent unintended overwrites and ensure user-defined configurations survive repeated apply_user_properties calls. Implemented update_specific_default_properties to preserve user settings while applying default optimizations, validated against GPU execution configurations, and linked to a targeted commit for traceability.

December 2024 monthly summary for aobolensk/openvino: Delivered a high-impact OpenCL kernel optimization for Rope operations, achieving about 50% latency reduction across multiple models and configurations. This work replaced the reference kernel with an optimized version and updated test configurations to validate performance gains, directly improving inference speed and resource efficiency across models including Qwen7b, ChatGLM, Llama2, and Flux.