
During October 2024, Cyzero Kim developed GPU-accelerated POOL2D support for MobileVLM/CLIP inference in the whisper.cpp and llama.cpp repositories. Leveraging C++, Vulkan, and GLSL, Kim designed and integrated a dedicated POOL2D shader and updated the Vulkan pipeline, enabling efficient pooling operations on the GPU. This work reduced inference latency from approximately 2.8 seconds on CPU to 0.7 seconds on GPU, supporting near real-time performance and lowering CPU utilization. Kim also addressed parameter ordering to ensure correct operation sequencing. The engineering demonstrated strong depth in GPU programming and shader development, delivering measurable improvements in throughput and cost efficiency.
October 2024 performance summary: Implemented GPU-accelerated POOL2D support in Vulkan backends for MobileVLM/CLIP inference across whisper.cpp and llama.cpp. Delivered a dedicated POOL2D shader and pipeline, plus a parameter-ordering fix, enabling substantial latency reductions and throughput improvements. The work delivers clear business value by enabling near real-time inference, reducing CPU load, and lowering per-inference costs at scale.
October 2024 performance summary: Implemented GPU-accelerated POOL2D support in Vulkan backends for MobileVLM/CLIP inference across whisper.cpp and llama.cpp. Delivered a dedicated POOL2D shader and pipeline, plus a parameter-ordering fix, enabling substantial latency reductions and throughput improvements. The work delivers clear business value by enabling near real-time inference, reducing CPU load, and lowering per-inference costs at scale.

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