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Products that do what EVOLVE does

Gpu ray-tracing benchmark

  1. 1AG

    Thought the resources for GPU arch were lacking, so here we are

    Apr 2026 · jaso1024.com

  2. 2AR

    I’ve been experimenting with Rust lately and wanted a project that would help me explore some of its lower-level and performance-oriented features. Inspired by Sebastian Lague’s videos, I decided to implement my own ray tracer from scratch. The initial goal was just to render a simple 3D scene in the browser at a reasonable frame rate. It evolved into a small renderer that can: • Run locally or on the web using wgpu and WebAssembly • Perform mesh rendering with a Bounding Volume Hierarchy (BVH) for acceleration • Simulate both direct and indirect illumination for photorealistic results • Be…

    Nov 2025 · github.com

  3. 3IV

    This is a GPU "software" raytracer (i.e. using manual ray-scene intersections and not RTX) written using the WebGPU API that renders glTF scenes. It supports many materials, textures, material & normal mapping, and heavily relies on multiple importance sampling to speed up convergence.

    2024 · github.com

  4. 4CW

    Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points. The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU: - LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series) The result: 1M points at 60fps with smooth zoom/pan. Live demo:…

    Jan 2026 · github.com

  5. 5SA
  6. 6

    Supercharge gaming with DLSS 4, NVIDIA Studio, and AI

    2025

  7. 7

    C++ Path Tracer from scratch with zero third-party libraries. - themartiano/luz

    Jun 2026 · github.com

  8. 8

    Ultra-fast and customizable Python charts. Contribute to reflex-dev/xy development by creating an account on GitHub.

    Jul 2026 · github.com

  9. 9VA
  10. 10

    Scalable High-Quality 3D Asset Generation

    2024

  11. 11G2

    Back in the old days, people used to do general-purpose GPU programming by using shaders like GLSL. This is what inspired NVIDIA (and other companies) to eventually create CUDA (and friends). This is an implementation of GPT-2 using WebGL and shaders. Enjoy!

    2025 · github.com

  12. 12UA

    The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…

    Apr 2026 · systalyze.com

  13. 13GC

    Hi HN, YC w24 company here. We just pivoted from drone delivery to build gpudeploy.com, a website that routes on-demand traffic for GPU instances to idle compute resources. The experience is similar to lambda labs, which we’ve really enjoyed for training our robotics models, but their GPUs are never available for on-demand. We are also trying to make it more no-nonsense (no hidden fees, no H100 behind “contact sales”, etc.). The tech to make this work is actually kind of nifty, we may do an in-depth HN post on that soon. Right now, we have H100s, a few RTX 4090s and a GTX 1080 Ti online.…

    2024 · gpudeploy.com

  14. 14AP

    We ported pbrt-v4 to Julia and built it into a Makie backend. Any Makie plot can now be rendered with physically-based path tracing. Julia compiles user-defined physics directly into GPU kernels, so anyone can extend the ray tracer with new materials and media - a black hole with gravitational lensing is ~200 lines of Julia. Runs on AMD, NVIDIA, and CPU via KernelAbstractions.jl, with Metal coming soon. Demo scenes: github.com/SimonDanisch/RayDemo

    Feb 2026 · makie.org

  15. 15SS

    Hi HN, we are Ed, Zach, and Ronald, creators of Shadeform (https://www.shadeform.ai/), a GPU marketplace to see live availability and prices across the GPU market, as well as to deploy and reserve on-demand instances. We have aggregated 8+ GPU providers into a single platform and API, so you can easily provision instances like A100s and H100s where they are available. From our experience working at AWS and Azure, we believe that cloud could evolve from all-encompassing hyperscalers (AWS, Azure, GCP) to specialized clouds for high-performance use cases. After the launch of…

    2023 · shadeform.ai

  16. 161T
  17. 17NG
  18. 18PB
  19. 19

    Real-time GPU performance benchmark online

    Sep 2025

  20. 20MG

    I run a small project called best-gpu.com, a site that ranks GPUs by price-to-performance. While browsing PC building forums and Reddit, I kept seeing the same question: “What should I upgrade to from my current GPU?” Most answers are just lists of cards without showing the actual performance gain, so people often end up paying for upgrades that barely improve performance. So I built a small tool: a GPU Upgrade Calculator. You enter your current GPU and it shows: estimated performance gain a value score based on price vs performance a filtered list of upgrade options (brand, price, VRAM,…

    Mar 2026 · best-gpu.com

  21. 21

    Hi HN, I built PantheonGPU because I wanted a better way to answer a simple question: is this GPU actually healthy and performing the way it should? A GPU can show normal temperatures and utilization and still be underperforming, unstable under certain workloads, or have memory, PCIe, or configuration issues. PantheonGPU actively tests the GPU instead of only monitoring telemetry. It currently includes 45+ tests covering compute, tensor workloads, memory, cache, PCIe, thermals, stability, and AI/LLM inference. It supports both NVIDIA CUDA and AMD ROCm. I’m also exploring a larger use…

    20d ago · pantheongpu.com

  22. 22DO

    Demo of agent based model on GPU with CUDA and OpenGL (Windows/Linux) Agent instances on GPU memory Uses SSBO for instanced objects (with GLSL 450 shaders) CUDA OpenGL interops Renders with GLFW3 window manager Dynamic camera views in OpenGL (pan,zoom with mouse) Libraries installed using vcpkg (https://github.com/KienTTran/ABMGPU)

    2023 · github.com

  23. 23AG

    This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.

    2025 · rlafuente.com

  24. 241A

    We added a new feature to our open-source HTML5 game engine today that allows you to create Sprite GPU Layers. These pack together simple JS object definitions onto the GPU and renders them using a custom vertex shader. This skips all CPU computation and GPU upload operations, resulting in vastly increased performance. For objects that don't need to be updated by input, physics, or other interactive behaviors, we found it to be a great solution. In testing, we've easily managed to blast millions of sprites around on moderate desktop-grade GPUs. Indeed, we hit the fill rate limitation of the…

    2025 · phaser.io

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