Search Results for "hardware" - Page 4
Sort By:
Showing 522 open source projects for "hardware"
View related business solutions-
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
-
1
Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. Chitu is designed to scale from small single-machine deployments to large distributed clusters that handle high volumes of concurrent inference requests. ... -
2
Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. ... -
3
SAM 2
The repository provides code for running inference with SAM 2
...It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time interactivity even on larger images or constrained hardware. SAM2 comes with pretrained weights and easy-to-use APIs, enabling developers and researchers to integrate promptable segmentation into annotation tools, vision pipelines, or downstream tasks. The project also includes scripts and notebooks to compare SAM2 against SAM on edge cases, benchmarks showing improvements, and evaluation suites to measure mask quality metrics like IoU and boundary error. -
4
Qiskit
Qiskit is an open-source SDK for working with quantum computers
Qiskit [kiss-kit] is an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules. When you are looking to start Qiskit, you have two options. You can start Qiskit locally, which is much more secure and private, or you get started with Jupyter Notebooks hosted in IBM Quantum Lab. Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
5
Speakr
Speakr is a personal, self-hosted web application
...Behind the scenes, Speakr leverages modern TTS engines and streaming audio technologies to deliver smooth and responsive speech generation without noticeable delay. The project is built with extensibility in mind, enabling developers to add custom voices, integrate additional languages, and tailor the backend for different hardware or cloud environments. It also supports saving generated audio as downloadable files so users can reuse the speech outputs in other projects, presentations, or media content. -
6
Nano PDF Editor
Edit PDF files with Nano Banana
Nano PDF Editor is a minimalist, portable PDF viewer and toolkit that focuses on simplicity, speed, and ease of integration for applications that need basic PDF rendering without heavy dependencies. It provides core functionality such as page navigation, zooming, text selection, and rendering directly to native graphics surfaces, making it suitable for lightweight PDF viewing scenarios on desktop or embedded platforms. Designed to be easily embedded into larger software projects, Nano-PDF... -
7
BioNeMo
BioNeMo Framework: For building and adapting AI models
BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications. -
8
Chatterbox TTS Server
Self-host the powerful Chatterbox TTS model
...It also includes OpenAI-compatible API behavior, which helps developers connect it to existing tools that already expect that style of endpoint. The server can run on NVIDIA CUDA, AMD ROCm, or CPU, giving it flexibility across different hardware setups. Its main value is packaging a powerful TTS workflow into a practical service that can be accessed through a browser or integrated into other software. -
9
clone-voice
A sound cloning tool with a web interface, using your voice
Clone-voice is a local voice-cloning tool that lets you synthesize speech in any target voice or convert one recording into another voice using the same timbre. It is built around Coqui’s XTTS-v2 model, so it inherits multilingual support and modern neural TTS quality while wrapping it in a user-friendly desktop workflow. The app is designed to be very easy to use: you download a precompiled package, double-click app.exe, and it launches a browser-based web interface where you control... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
handy-ollama
Implement CPU from scratch and play with large model deployments
handy-ollama is an open-source educational project designed to help developers and AI enthusiasts learn how to deploy and run large language models locally using the Ollama platform. The repository serves as a structured tutorial that explains how to install, configure, and use Ollama to run modern language models on personal hardware without requiring advanced infrastructure. A key focus of the project is enabling users to run large models even without GPUs by leveraging optimized CPU-based inference pipelines. The project includes step-by-step guides that walk learners through tasks such as installing Ollama, managing local models, calling model APIs, and building simple AI applications on top of locally hosted models. ... -
11
mergekit
Tools for merging pretrained large language models
...This approach allows researchers to combine specialized models into a more versatile system capable of performing multiple tasks. mergekit implements a variety of merging algorithms and strategies that control how model parameters are blended together during the merging process. The library is designed to operate efficiently even in environments with limited hardware resources by using memory-efficient processing methods that can run entirely on CPUs. It also provides configuration-driven workflows that allow users to experiment with different merging strategies without modifying source code. -
12
TurtleBot3
ROS packages for Turtlebot3
A platform for developing robotics applications, TurtleBot3 offers an open-source kit for research, education, and development in robotics with ROS (Robot Operating System). -
13
LuxTTS
A high-quality rapid TTS voice cloning model
LuxTTS is an open-source text-to-speech (TTS) system focused on delivering high-quality, rapid voice synthesis and voice cloning that runs extremely fast and efficiently on consumer hardware. It implements a lightweight architecture based on ZipVoice and optimized sampling techniques so that it can generate speech at speeds up to roughly 150 times real-time on a single GPU and faster than real-time on CPU, all while producing audio at high fidelity with 48 kHz quality. The project supports zero-shot voice cloning, meaning it can adapt to a reference speaker’s voice with minimal example data, enabling realistic and personalized synthetic speech. ... -
14
LoLLMs Hub Fortress
A proxy server for multiple ollama instances with Key security
...The architecture is built around a hierarchical “master and slave” hub model, enabling distributed deployments where multiple machines or clusters can be managed through a single entry point. This design allows organizations to scale horizontally, combining local hardware, cloud resources, and specialized inference servers into a unified infrastructure. LoLLMs Hub also introduces intelligent routing mechanisms that automatically select the most appropriate model based on rules such as priority, load balancing, or availability, improving efficiency and reliability. -
15
OuteTTS
Interface for OuteTTS models
OuteTTS is an interface library for running OuteTTS text-to-speech models across a range of backends, making it easier to deploy the same model on different hardware and runtimes. It provides a high-level Interface API that wraps model configuration, speaker handling, and audio generation so you can focus on integrating speech into your application rather than wiring up low-level engines. The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face Transformers, ExLlamaV2, VLLM and a JavaScript interface via Transformers.js, allowing it to run on CPUs, NVIDIA CUDA GPUs, AMD ROCm, Vulkan-capable GPUs, and Apple Metal. ... -
16
Lemonade
Lemonade helps users run local LLMs with the highest performance
Lemonade is a local LLM runtime that aims to deliver the highest possible performance on your own hardware by auto-configuring state-of-the-art inference engines for both NPUs and GPUs. The project positions itself as a “local LLM server” you can run on laptops and workstations, abstracting away backend differences while giving you a single place to serve and manage models. Its README emphasizes real-world adoption across startups, research groups, and large companies, signaling a focus on practical deployments rather than toy demos. ... -
17
Train LLM From Scratch
A straightforward method for training your LLM
...The repository walks through the process from downloading data to generating text with a trained model. It supports training smaller or larger models, including million- and billion-parameter configurations depending on available hardware. A major goal is accessibility, since the author frames it as possible to train models using a single GPU. It is most useful for learners, researchers, and developers who want practical exposure to LLM internals. -
18
BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
...By limiting weight precision while maintaining efficient scaling and normalization strategies, the architecture aims to retain competitive performance while significantly reducing hardware requirements. -
19
The FreeMoCap Project
Free Motion Capture for Everyone
FreeMoCap is an open-source markerless motion capture system that enables users to record human movement using ordinary cameras and convert the footage into usable 3D motion data. The project’s goal is to democratize motion capture by removing the need for expensive suits or proprietary studio hardware, instead relying on computer vision and pose estimation pipelines. It processes synchronized video feeds to reconstruct skeletal motion, which can then be exported for animation, biomechanics research, or creative projects. FreeMoCap includes tools for calibration, recording, processing, and visualization, allowing users to move from raw footage to structured motion data within a single ecosystem. ... -
20
LightAutoML
Fast and customizable framework for automatic ML model creation
LightAutoML is an automated machine learning (AutoML) framework optimized for efficient model training and hyperparameter tuning, focusing on both tabular and text data. -
21
ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model
...The project provides inference code, demos (command line, web, API), quantization support for lower memory deployment, and tools for finetuning (e.g., via P-Tuning v2). It is optimized for dialogue and question answering with a balance between performance and deployability in consumer hardware settings. Support for quantized inference (INT4, INT8) to reduce GPU memory requirements. Automatic mode switching between precision/memory tradeoffs (full/quantized). -
22
OpenDAN
OpenDAN is an open source Personal AI OS
OpenDAN is an open-source Personal AI OS , that consolidates various AI modules in one place for your personal use. The goal of OpenDAN (Open and Do Anything Now with AI) is to create a Personal AI OS , which provides a runtime environment for various Al modules as well as protocols for interoperability between them. With OpenDAN, users can securely collaborate with various AI modules using their private data to create powerful personal AI agents, such as butlers, lawyers, doctors, teachers,... -
23
Intel Extension for PyTorch
A Python package for extending the official PyTorch
Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* xpu device. -
24
GLM-4.1V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
...Though smaller in scale, GLM-4.1V maintains competitive performance, particularly impressive on many benchmarks for models of its size: in fact, on a number of multimodal reasoning and vision-language tasks it outperforms some much larger models from other families. It represents a trade-off: somewhat reduced capacity compared to 4.5V or 4.6V, but with benefits in terms of speed, deployability, and lower hardware requirements — making it especially useful for developers experimenting locally, building lightweight agents, or deploying on limited infrastructure. Given its open-source availability under the same project repository, it provides an accessible entry point for testing multimodal reasoning and building proof-of-concept applications. -
25
Stable Diffusion web UI for AMDGPUs
Stable Diffusion WebUI optimized for AMD GPUs with editing tools
Stable Diffusion WebUI AMDGPU is a browser-based interface for generating images using Stable Diffusion, built with Gradio and adapted for AMD graphics hardware. It provides both text-to-image and image-to-image workflows, allowing users to create, refine, and upscale visuals within a single interface. It includes tools such as inpainting and outpainting for editing specific areas of an image, along with features like prompt matrix generation and attention controls to fine-tune outputs. ...
