CUDA matrix multiplication benchmarking on Jetson Orin Nano. Four implementations, three power modes, five matrix sizes. 99.5% mathematical validation. C++/CUDA and Python.

🎓 CUDA HPC Kernel Optimization Lab: Progressive GEMM, FlashAttention, Tensor Core & CUDA 13 Features | 从朴素到 Tensor Core 的 CUDA 高性能算子优化实验室

A 110M-parameter Llama-style transformer trained from scratch on the TinyStories dataset, optimized for high-throughput training on 4GB VRAM consumer GPUs. The project features a custom asynchronous CUDA-stream prefetcher and KV-cache inference, achieving 10k+ TPS on an RTX 3050.

🔍 Analyze CUDA matrix multiplication performance and power consumption on NVIDIA Jetson Orin Nano across multiple implementations and settings.

High-performance matrix engine for Unit-Domain Flow (UDF). Eliminates Mantissa Friction with 0.00 MSE integrity.

Hardened RAG pipeline with Llama 3.2 (3B) & Arize Phoenix. Features 4-bit Unsloth optimization, OpenTelemetry auditing, and a KV-cache stability patch for T4 GPUs. P99 Latency: 19.2s.