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

Standalone LLM inference benchmarking pipelines on AMD GPUs using ROCm, vLLM, MAD, and data visualization scripts.

Hands-on Jupyter notebooks for deep learning with TensorFlow, covering fundamental concepts, model training, and applied tabular projects.

Plots GPU benchmarks across user-selected axes and highlights the Pareto frontier to help builders choose hardware for local AI inference without editorial bias.

Artifact-backed LLM serving performance lab for vLLM baselines, official metrics, GuideLLM checks, and SGLang/PD scaffolding

One-shot script to audit GPU, CUDA, PyTorch, CPU, and disk performance before debugging a slow or broken ML environment.

benchHUB is a Python-based project to parse, aggregate, and visualize system and performance benchmarks. It includes a Streamlit dashboard to display and compare results.

Run a 2-min local benchmark → predict how long your AI job will take on cloud GPU (T4/V100/A100). No guessing, no wasted money.

Reproducible GPT-2 distributed-training benchmarks on 1-8 V100 GPUs using Slurm, PyTorch, DeepSpeed, NCCL, NVTX, and Nsight Systems.

Ramanujan's mathematics meets the NVIDIA stack: CUDA-Q/cuQuantum quantum simulation + NIM/Nemotron analysis, consumer RTX to cloud H100