Search Results for "gpu hardware"
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Showing 159 open source projects for "gpu hardware"
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GPU Puzzles
Solve puzzles. Learn CUDA
GPU Puzzles is an educational project designed to teach GPU programming concepts through interactive coding exercises and puzzles. Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. -
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GPU Hot
Real-time NVIDIA GPU dashboard
GPU Hot is an open-source, lightweight monitoring dashboard designed to provide real-time visibility into NVIDIA GPU performance across single machines or entire clusters. The project offers a self-hosted web interface that streams hardware metrics directly from GPU servers, enabling developers, ML engineers, and system administrators to observe GPU utilization and system behavior in real time through a browser. -
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NVIDIA GPU Operator
NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes
Kubernetes provides access to special hardware resources such as NVIDIA GPUs, NICs, Infiniband adapters and other devices through the device plugin framework. However, configuring and managing nodes with these hardware resources requires the configuration of multiple software components such as drivers, container runtimes or other libraries which are difficult and prone to errors. -
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ChefKiss Inferno
Emulating Apple Silicon devices
Inferno by ChefKissInc is a low-level systems project focused on enabling hardware acceleration and advanced graphics compatibility on Apple Silicon devices, particularly within unsupported or experimental environments. It is designed to bridge gaps between macOS hardware capabilities and software ecosystems that traditionally rely on different GPU architectures, such as those found in Linux or Windows environments. -
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CubeCL
Multi-platform high-performance compute language extension for Rust
CubeCL is a low-level compute language and compiler framework designed to simplify and optimize GPU programming for high-performance workloads, particularly in machine learning and numerical computing. It provides an abstraction layer that allows developers to write portable, hardware-efficient compute kernels without directly dealing with complex GPU APIs such as CUDA or OpenCL. CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. ... -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. ... -
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CUDA-Q
C++ and Python support for the CUDA Quantum programming model
...It provides a full toolchain that includes compilers, runtimes, and libraries for writing quantum programs in both C++ and Python. The platform is designed to be hardware-agnostic, allowing developers to run applications on different quantum backends or simulate them efficiently using GPU acceleration when physical quantum hardware is unavailable. It enables complex workflows where classical and quantum computations are tightly integrated, supporting advanced research and real-world applications in quantum computing. ... -
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SwiftShader
SwiftShader is a high-performance CPU-based implementation
SwiftShader is Google’s high-performance CPU-based implementation of the Vulkan 1.3 graphics API, designed to provide a hardware-independent rendering solution for 3D graphics. Unlike traditional GPU drivers, SwiftShader executes graphics commands entirely on the CPU, making it ideal for environments where dedicated graphics hardware is unavailable or unsuitable. It acts as a drop-in replacement for Vulkan drivers, allowing existing applications to run seamlessly by redirecting API calls through its software-based rendering engine. ... -
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GPUStack
Performance-optimized AI inference on your GPUs
GPUStack is an open-source GPU cluster management platform designed to simplify the deployment and operation of artificial intelligence models across heterogeneous hardware environments. The system aggregates GPU resources from multiple machines into a unified cluster so developers and administrators can run large language models and other AI workloads efficiently across distributed infrastructure. -
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Stats
macOS system monitor in your menu bar
Stats currently supported on macOS 10.13 (High Sierra) and higher. Stats is an application that allows you to monitor your macOS system. CPU utilization, GPU utilization, memory usage, disk utilization, sensors information (Temperature/Voltage/Power), battery level, network usage, fans speed, fan control, and Bluetooth devices. Supports many languages, such as English, Polski, Українська, Русский, and many more. You can help by adding a new language or improve existing translation. -
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LibreHardwareMonitor
Monitor temperature sensors, fan speed, voltage, load & clock speeds
...LibreHardwareMonitor supports modern Intel and AMD CPUs, major GPU vendors, storage devices, and network adapters. Built on modern .NET versions, it continues to evolve with frequent updates and broad community contributions. Licensed under MPL 2.0, it offers a transparent and extensible alternative to proprietary hardware monitoring tools. -
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FlexLLMGen
Running large language models on a single GPU
FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. -
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XenosRecomp
A tool for converting Xbox 360 shaders to HLSL
XenosRecomp is a specialized project within the Hedge-dev ecosystem that focuses on recompiling and reconstructing the Xenos GPU pipeline used in the Xbox 360, enabling accurate rendering when porting games to modern platforms. It works alongside CPU recompilation tools by translating GPU-specific instructions and behaviors into equivalents that can be executed on modern graphics APIs such as DirectX or Vulkan. This allows recompiled games to maintain visual fidelity while benefiting from modern hardware acceleration. ... -
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AirLLM
AirLLM 70B inference with single 4GB GPU
AirLLM is an open source Python library that enables extremely large language models to run on consumer hardware with very limited GPU memory. The project addresses one of the main barriers to local LLM experimentation by introducing a memory-efficient inference technique that loads model layers sequentially rather than storing the entire model in GPU memory. This layer-wise inference approach allows models with tens of billions of parameters to run on devices with only a few gigabytes of VRAM. ... -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
...HeavyDB was originally developed as part of the OmniSci platform (formerly MapD) and is commonly used for large-scale analytics and geospatial data processing. The database compiles queries into optimized machine code that executes efficiently on GPU hardware, significantly accelerating analytical workloads. It supports hybrid deployment environments where queries can run on both CPU and GPU architectures depending on the available resources. -
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PowerInfer
High-speed Large Language Model Serving for Local Deployment
...PowerInfer incorporates specialized algorithms and sparse operators to manage neuron activation patterns and minimize data transfers between hardware components. As a result, it enables powerful language models to run on consumer hardware while achieving performance comparable to more expensive server-grade systems. -
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LocalAI
The free, Open Source alternative to OpenAI, Claude and others
LocalAI is an open-source platform that allows users to run large language models and other AI systems locally on their own hardware. It acts as a drop-in replacement for APIs such as OpenAI, enabling developers to build AI-powered applications without relying on external cloud services. The platform supports a wide range of model types, including text generation, image creation, speech processing, and embeddings. LocalAI can run on consumer-grade hardware and does not necessarily require a GPU, making it accessible for local development and private deployments. ... -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
...Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations such as reductions, element-wise computations, softmax, and attention mechanisms. These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. The repository also contains extensive learning notes that summarize CUDA programming concepts, GPU architecture details, and performance engineering strategies. -
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Parallax
Parallax is a distributed model serving framework
Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to available hardware and how requests are routed across nodes during execution. ... -
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GPT4All
Run Local LLMs on Any Device. Open-source
GPT4All is an open-source project that allows users to run large language models (LLMs) locally on their desktops or laptops, eliminating the need for API calls or GPUs. The software provides a simple, user-friendly application that can be downloaded and run on various platforms, including Windows, macOS, and Ubuntu, without requiring specialized hardware. It integrates with the llama.cpp implementation and supports multiple LLMs, allowing users to interact with AI models privately. This... -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
...Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. ... -
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node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama
...By using native bindings and optimized model execution, the framework allows developers to integrate advanced language model capabilities into desktop applications, server software, and command-line tools. The system automatically detects the available hardware on a machine and selects the most appropriate compute backend, including CPU or GPU acceleration. Developers can use the library to perform tasks such as text generation, conversational chat, embedding generation, and structured output generation. Because it runs models locally, the platform is particularly useful for privacy-sensitive environments or offline AI deployments. -
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Scalene
High-performance CPU, GPU, and memory profiler for Python
Scalene is a high-performance CPU, GPU and memory profiler for Python that does a number of things that other Python profilers do not and cannot do. It runs orders of magnitude faster than other profilers while delivering far more detailed information. Once Scalene has profiled your program, it will launch a web browser with an interactive user interface (all processing is done locally). Hover over bars to see breakdowns of CPU and memory consumption, and click on underlined column headers... -
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Starling Framework
2D GPU-accelerated framework for ActionScript developers
Starling is an open-source 2D framework for ActionScript developers that leverages GPU acceleration via Adobe's Stage3D API to create smooth, high-performance games and applications across desktop and mobile platforms. It mimics the traditional Flash display list while dramatically improving performance, making it a popular choice for Flash developers transitioning into more efficient, hardware-accelerated environments. -
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SkyPilot
SkyPilot: Run AI and batch jobs on any infra
SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability. Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
