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

3DMark Professional -- GPU and gaming PC benchmark suite with DirectX 12 tests, ray-tracing benchmark, stress tests, and detailed score reports. Pro version with full features. Trial available. Compatible with Windows 10/11 (64-bit). Updated 2026.

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.

Benchmark CPU, Benchmark GPU, Storage, RAM using Python

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.

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

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

**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.

Platform-agnostic benchmark harness for LLM inference endpoints. Measures TTFT, throughput, and failure rate against any OpenAI-compatible /v1/completions API (vLLM, SGLang, Baseten, RHOAI, …) and recommends a vLLM config grounded in real benchmark data.

🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.

Implementing and Visualizing Deep Learning Models

A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.

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

benchmark scaled LLM inference and training

OpenCL benchmarking tool to measure host-device bandwidth and kernel global memory throughput across GPUs and CPUs.

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

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.

Benchmark your GPU against any GGUF model and contribute to the public leaderboard. Measures throughput, TTFT, ITL, and VRAM limits across quantizations and context sizes.