I specialize in High-Performance Computing (HPC) and Large Language Model (LLM) Optimization. My passion lies in pushing the boundaries of hardware constraints through algorithmic innovation and surgical memory management.

Currently, I am building next-generation inference engines that enable massive AI models to run on consumer-grade hardware.

Adaptive Hybrid Quantization Framework for Low-VRAM Devices

I am the creator of QKV Core, a groundbreaking framework designed to democratize AI.

• The Problem: Running 7B+ parameter models typically requires expensive enterprise GPUs (24GB+ VRAM).
• My Solution: Developed a "Surgical Alignment" and "Adaptive Hybrid Compression" algorithm that eliminates memory fragmentation.
• The Result: Achieved 34% faster I/O and enabled 7B models to run seamlessly on a 4GB GTX 1050, strictly adhering to physical memory limits.

I don't just use libraries; I optimize them at the kernel level.

I am open to collaborations on Open Source AI, Model Compression, and Fintech Security.

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