gpu-benchmark

The Ultimate 3DMark Professional Guide 2026 - GPU Benchmarking and Performance Testing

This is a uer-friendly Python codebase designed for stress testing of Nvidia GPUs, intel CPUs, and AMD CPUs in various modes

An stress and benchmark utility for NVIDIA GPUs. Measures performance across various precisions (FP64, FP32, TF32, FP16, INT8) and monitors real-time vitals like power, temperature, and clock speeds.

nvProbe — Open-source NVIDIA GPU benchmark suite for CUDA workload automation, Slurm HPC cluster profiling, and MLPerf reporting

PCBench is a versatile Python-based system performance benchmarking tool designed to empower users with insights into their hardware's capabilities. Whether you're a tech enthusiast, a PC gamer, or a developer optimizing your code, PCBench provides comprehensive benchmarking for both CPUs and GPUs.

Benchmark CPU, Benchmark GPU, Storage, RAM using Python

High-performance GPU benchmarking tool built with Vulkan, CUDA, and ImGui — featuring real-time physics simulation, custom rendering, and modular engine architecture.

Advanced benchmark harness for AI agent inference workloads on AMD GPU and ROCm cloud infrastructure

Re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms.

**Kernel-V8** is a high-performance GPU benchmarking engine built on the WebGL2 API. By rendering a complex 8th-order **Mandelbulb** fractal in real-time, it generates intense arithmetic workloads to evaluate the stability, thermal throttling, and peak compute throughput of modern graphics hardware.

Reproducible SRAM surrogate simulation benchmark with CPU/CUDA lanes, fidelity validation, and GPU portability architecture.

Standalone C++17 SYCL benchmarks for arithmetic, joint-matrix, device-memory, and USM transfer throughput without external compute libraries.

A code to benchmark GPU performance on different models

🌌 High-performance WebGL Stress Test. Advanced Raymarching fractal engine with real-time RGB shading and kernel injection.

🏆 Which 3DGS renderer is fastest? Which compression is best? We measured them all — on the same GPU, same scenes, same protocol.

PyTorch scripts to benchmark CPU vs GPU matrix multiplication and monitor CPU/GPU stats during a live CIFAR100 training run

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.

GPU vs CPU performance benchmarking for PyTorch and JAX. Works on AMD ROCm, DirectML, CUDA, MPS, CPU. Optimized for RX 5700 XT in WSL2.

Poor Paul's Benchmark as an MCP server. Queryable GPU inference data — quantization, throughput, VRAM, concurrent users — for Claude Desktop, Cursor, Windsurf, Cline, and any MCP client. Self-host or use the free hosted endpoint.

GPU benchmark suite for AI inference workloads. Test throughput, latency, and power efficiency across NVIDIA, AMD, and Apple Silicon. By Petronella Technology Group.