June 2026 Monthly Performance Summary for aobolensk/openvino. Focused on delivering a high-impact kernel optimization to improve inference performance. Key feature delivered: Gated Delta Network (GDN) kernel performance optimization using vectorized loading (vload) and fused multiply-add (FMA) to boost computational efficiency. No major bugs fixed this month. Overall impact includes faster GDN computations, improved throughput and lower latency for deployment workloads, and potential cost savings due to reduced compute time. Technologies and skills demonstrated include GPU/kernel optimization, vectorization techniques, FMA usage, and performance-driven coding practices. AI-assisted kernel optimization guidance was leveraged to identify and apply improvements. Ticket CVS-188110 tracked for the work.

May 2026 — Delivered enabling Grouped Query Attention (GQA) in Stateful GDN for openvinotoolkit/openvino. This feature allows grouped queries with variable head sizes, improving flexibility and performance for multi-head attention scenarios. Implemented CPU-path support and added dedicated tests to validate behavior and regression safety. This work expands GQA support in core inference paths and sets the stage for broader backend optimizations and future GQA extensions.

Month: 2026-04 — Delivered CPU and GPU kernels for PagedGatedDeltaNet in OpenVINO, enabling efficient handling of variable-length subsequences in attention and optimizing inference on Intel hardware. Implemented and merged CPU kernel (PR 35070) and GPU kernel (PR 35071) with associated tests, both referencing the operation spec defined in PR 35051. This work reduces latency and increases throughput for attention-heavy models on Intel CPUs/GPUs, expands supported workloads, and improves maintainability with added test coverage. No critical hotfixes required this month; the initiative strengthens the product's capability to run dynamic sequences and aligns with CVS-183801 and CVS-183804, with ongoing collaboration across teams to ensure quality and future-proof integration.

March 2026 performance summary: Delivered cross-architecture GatedDeltaNet support across CPU and GPU to enable broader deployment and faster inference. CPU work added an internal GatedDeltaNet operation, fusion, and a dedicated CPU kernel, linked to CVS tickets CVS-180287, CVS-180289, CVS-180880 (commit f958066663e4f29f1b1ab9f42ba7779ee12b50d9). GPU work enabled the GatedDeltaNet kernel on GPU with validation tests, linked to CVS-180881 (commit 18792ac0b93b217639181c2e4bef202df8a905e1); co-authored-by Chenhu Wang. This work expands platform support, establishes end-to-end functionality, and provides a foundation for future optimizations and deployments across environments.

Monthly summary for 2025-12: Delivered Rotary Positional Embeddings (RoPE) support for GLM4 in openvino, with CPU-optimized tensor operations and configurable cosine/sine input handling to boost performance and flexibility for sequential data workloads. The work strengthens CPU backends support and positions GLM4 for broader production deployment.

November 2025 performance and capability enhancements for the openvino repository focused on throughput, resource efficiency, and model configurability. Key work spans GPU-accelerated interpolation, extended VL_SDPA head-size padding, and GPT-OSS style Rope fusion for rotary embeddings across CPU and GPU, delivering tangible business value through faster inference, better memory alignment, and broader model support.

2025-07 monthly summary: Stabilized CPU-based attention paths in OpenVINO with a focus on large-model inference reliability and configurability. Delivered Scaled Attention robustness fixes for vLLM (block_num calculation, sequence-length and threading handling) with test updates, and added Sage Attention support to Paged Attention via ENABLE_SAGE_ATTN (config handling, cache precision adjustments, Sage-specific quantization, and i8 support in brgemm). These efforts improve accuracy, throughput, and deployment readiness for production workloads.

Monthly summary for 2025-05 focused on business value and technical achievements in the aobolensk/openvino repository. This period centered on robustness fixes for attention and quantization paths, stabilizing the CPU backend, and expanding test coverage to prevent regressions. Key work delivered incremental yet impactful reliability improvements that directly affect production inference quality and uptime.

April 2025 performance summary for the OpenVINO projects (aobolensk/openvino and openvinotoolkit/openvino.genai). The team delivered targeted robustness and performance improvements in CPU paths, focusing on attention kernels and quantization pipelines. The work enhances reliability for quantized inference on CPU and reduces memory footprint for 4-bit data, enabling faster, more cost-effective model deployment on mainstream hardware.

March 2025: Focused on stabilizing and extending the Intel CPU plugin quantization path in openvino, delivering a new unsigned-8 quantization path for key caches and fixing critical attention-path correctness and precision issues. The work improves inference reliability, speed on CPU, and deployment readiness for attention-based models.

February 2025 (2025-02) performance summary for aobolensk/openvino: Delivered dynamic KV cache precision for PagedAttention in the CPU plugin, enabling runtime-configurable precision and cache shapes. Refactored ConvertPagedAttnInputs to derive KV cache precision and shape from runtime configurations, supporting different precision levels and potential performance gains. No major bugs fixed this month; focused on stability and groundwork for optimization. Impact: unlocks CPU-side performance tuning and better resource utilization; improves maintainability with targeted refactors. Technologies demonstrated include C++, CPU plugin development, runtime configuration, and dynamic precision handling.

Monthly summary for 2025-01: Implemented performance-oriented quantization feature and resolved numerical accuracy issues in the CPU path of OpenVINO, focusing on PageAttentionNode and float16 dequantization. The work reduces memory usage and increases CPU throughput for attention workloads, with higher numerical fidelity and easier configurability.

November 2024: Delivered critical improvements to the OpenVINO CPU/IR optimization workflow. Focused on correctness, configurability, and deployment stability. Key deliverables include a bug fix in the Rope mixed-precision path and the introduction of IR-driven runtime options for CPU models, enabling dynamic tuning without code changes.