execute shader programs
Modern GPU Compute Framework in Javascript
OpenClaw plugin - Remote GPU compute via FastAPI microservice (BERTScore, embeddings)
Unity integration utilities for OneJS - esbuild/PostCSS plugins for USS, GPU compute, and more
🍎Mavity: GPU compute particle systems and graphs, including Barnes-Hut physics (formerly three-g)
The luma.gl core Device API
RunPod GPU compute MCP server — pods, serverless endpoints, jobs, and GPU inventory with token-efficient defaults
Vultr GPU compute infrastructure MCP server — VMs, bare metal, serverless inference, DNS, firewall, snapshots, billing with token-efficient defaults
High-performance GPU compute for biological neural simulation with custom kernel support
Classify GPU's based on their benchmark score in order to provide an adaptive experience.
The engine that powers scroll-into-view-if-needed
Official JavaScript/TypeScript SDK for NeuraNET - Decentralized GPU Compute Network
WebGPU for Node.js via Google Dawn
Google Compute Engine Client Library for Node.js
Computes the greatest common divisor (gcd).
Computes the least common multiple (lcm).
[](https://www.npmjs.com/package/stats-gl) [](https://www.npmjs.com/package/st
Minimal WebGPU shader library
KTX 2.0 (.ktx2) parser and serializer.
This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
JavaScript SDK and CLI for building JavaScript applications on [Fastly Compute](https://www.fastly.com/products/edge-compute/serverless).
translation addon for qvac
Computes the dot product between two numeric arrays.
A generated SDK for ComputeManagementClient.
Pure-Rust wgpu GPU-compute abstraction for the COOLJAPAN ecosystem
GPU acceleration for mathematical computations
Simple GPU-Compute using WebGPU
A GPU-based layer renderer with configurable compute shaders for Eldiron.
Rust-native Infrastructure as Code — bare-metal first, BLAKE3 state, provenance tracing
Empowering anyone to leverage GPU-acceleration with as little barrier-to-entry as possible
Empowering anyone to leverage GPU-acceleration with as little barrier-to-entry as possible
Empowering anyone to leverage GPU-acceleration with as little barrier-to-entry as possible
Perfetto GPU Compute Injection — shared library
Ignis is the foundation of a CUDA-backed deep-learning ecosystem for Ruby that actually targets native Windows. It provides a GPU n-dimensional array (Ignis::NDArray), CUDA memory/device management, a runtime kernel compiler (NVRTC) with a batteries-included kernel library, fp16/bf16 conversion, and cuBLAS GEMM. Kernels are compiled at runtime and libraries are bound via FFI — there are NO C extensions, so installation needs no compiler or devkit (the usual Windows native-gem killer). Requires an NVIDIA GPU + CUDA toolkit/runtime.
Ruby bindings for the WebGPU API, providing GPU compute and graphics capabilities through the wgpu-native library.
A ruby library that searches the linux sysfs file system for compute unit devices such as CPUS, GPUs and other ASIC compute devices. Allows programmatic access to collect real time metrics from the kernel or relatated driver toolchain. Is meant to be used as a toolchain for future tools. This library also makes use of opencl library and requires the opencl_ruby_ffi gem.
ignis-autograd adds a differentiable tensor (Ignis::Tensor) and a reverse-mode autograd tape on top of the Ignis GPU foundation. Build computation graphs over GPU arrays and get exact gradients (verified against finite differences). The building block for neural-network training in pure Ruby on an NVIDIA GPU.
ignis-numerics is GPU numerical computing for Ruby on the Ignis foundation: dense/sparse linear algebra, FFT (cuFFT), random number generation (cuRAND), linear solvers & decompositions (cuSOLVER), and Einstein-notation tensor contraction (cuTENSOR), with an nvmath-style API (Ignis.fft, Ignis.solve, Ignis.zeros, …). The lineage NvRuby began from, revived as a standalone gem.
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