Search Results for "hardware"
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Showing 66 open source projects for "hardware"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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GPT4All
Run Local LLMs on Any Device. Open-source
...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 project also supports Python integrations for easy automation and customization. GPT4All is ideal for individuals and businesses seeking private, offline access to powerful LLMs. -
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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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MLC LLM
Universal LLM Deployment Engine with ML Compilation
MLC LLM is a machine learning compiler and deployment framework designed to enable efficient execution of large language models across a wide range of hardware platforms. The project focuses on compiling models into optimized runtimes that can run natively on devices such as GPUs, mobile processors, browsers, and edge hardware. By leveraging machine learning compilation techniques, mlc-llm produces high-performance inference engines that maintain consistent APIs across platforms. The system supports deployment on environments including Linux, macOS, Windows, iOS, Android, and web browsers while utilizing different acceleration technologies such as CUDA, Vulkan, Metal, and WebGPU. ... -
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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. ... -
Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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SillyTavern
LLM Frontend for Power Users
Mobile-friendly, Multi-API (KoboldAI/CPP, Horde, NovelAI, Ooba, OpenAI, OpenRouter, Claude, Scale), VN-like Waifu Mode, Horde SD, System TTS, WorldInfo (lorebooks), customizable UI, auto-translate, and more prompt options than you'd ever want or need. Optional Extras server for more SD/TTS options + ChromaDB/Summarize. SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters... -
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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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tt-metal
TT-NN operator library, and TT-Metalium low level kernel programming
tt-metal, also referred to in its documentation as TT-Metalium, is Tenstorrent’s low-level software development kit for programming applications on Tenstorrent AI accelerators. The project is designed for developers who need direct access to the company’s Tensix processor architecture, exposing a programming model that is closer to hardware control than high-level inference frameworks. Instead of following a traditional GPU model centered on massive thread parallelism, the platform is built around a grid of specialized compute nodes called Tensix cores, each with local SRAM, dedicated compute units, and multiple RISC-V control processors. The SDK provides the abstractions and APIs needed to manage data movement, compute kernels, memory coordination, and execution flow across this architecture. -
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mllm
Fast Multimodal LLM on Mobile Devices
...It also provides tools to convert models from popular formats like PyTorch checkpoints into optimized runtime formats that can be executed on supported hardware platforms. -
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nndeploy
An Easy-to-Use and High-Performance AI Deployment Framework
...The system supports multiple inference engines and hardware accelerators, allowing the same AI workflow to run on different platforms without significant modifications. nndeploy also includes performance optimization techniques such as parallel execution, memory reuse, and hardware-accelerated operations to improve inference speed. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Parallax
Parallax is a distributed model serving framework
...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. This scheduling system optimizes latency, throughput, and hardware utilization even when nodes have different computational capabilities. The platform also supports model sharding and pipeline parallelism, allowing very large models to run across distributed resources. -
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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. Instead of requiring complex orchestration systems such as Kubernetes, GPUStack provides a lightweight environment that automatically selects appropriate inference engines, configures deployment parameters, and schedules workloads across available GPUs. ... -
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FastDeploy
High-performance Inference and Deployment Toolkit for LLMs and VLMs
FastDeploy is an open-source inference and deployment toolkit designed to simplify the process of running and serving deep learning models across a wide range of hardware platforms. Developed within the PaddlePaddle ecosystem, the toolkit focuses on providing high-performance deployment capabilities for modern AI models including large language models and vision-language systems. The platform enables developers to deploy trained models quickly using optimized inference pipelines that support GPUs, specialized AI accelerators, and other hardware architectures. ... -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
...Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. By lowering the hardware requirements needed to work with large models, bitsandbytes helps make modern AI development more accessible to researchers and engineers. ... -
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Intel LLM Library for PyTorch
Accelerate local LLM inference and finetuning
Intel LLM Library for PyTorch is an open-source acceleration library developed to optimize large language model inference and fine-tuning on Intel hardware platforms. Built as an extension of the PyTorch ecosystem, the library enables developers to run modern transformer models efficiently on Intel CPUs, GPUs, and specialized AI accelerators. The framework provides hardware-aware optimizations and low-precision computation techniques that significantly improve the performance of large language models while reducing memory consumption. ... -
15
Phi-3-MLX
Phi-3.5 for Mac: Locally-run Vision and Language Models
Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips. -
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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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llama.cpp
LLM inference in C/C++
llama.cpp is a high-performance C and C++ project for running large language models locally and in the cloud with minimal setup. It is built around efficient inference, broad hardware support, and the GGUF model format. The project supports many model families and has become a major foundation for local AI tools, model serving, and embedded inference workflows. It provides command-line tools, a server mode with an OpenAI-compatible API style, model conversion utilities, and extensive backend acceleration options. llama.cpp runs on CPUs and GPUs, with support for Apple silicon, x86, RISC-V, CUDA, HIP, Vulkan, SYCL, Metal, and hybrid CPU-GPU execution. ... -
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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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mistral.rs
Fast, flexible LLM inference
...It provides multiple entry points for developers, including a CLI for running models locally and an HTTP server that exposes an OpenAI-compatible API surface for easy integration with existing clients. The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and device-mapping strategies, helping users get strong performance without manual tuning. It also supports serving multiple models from the same server process, enabling routing or quick switching between models depending on workload needs. For user-facing testing, mistral.rs can provide a built-in web UI, and it also offers a dedicated lightweight web chat interface that supports richer interaction patterns. -
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Clippy
Clippy, now with some AI
...Clippy integrates with the llama.cpp runtime to run models directly on a user’s computer without requiring cloud-based AI services. It supports models in the GGUF format, which allows it to run many publicly available open-source LLMs efficiently on consumer hardware. Users interact with the system through a simple animated assistant interface that can answer questions, generate text, and perform conversational tasks. The application includes one-click installation support for several popular models such as Meta’s Llama, Google’s Gemma, and other open models. -
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whichllm
Find the local LLM that actually runs and performs best
whichllm is a command-line tool for finding local large language models that can realistically run on a user’s hardware. It detects the machine’s available resources, including GPU, CPU, memory, and storage, then recommends models based on practical fit rather than parameter count alone. The project is useful for users who are unsure which local LLM will perform well on their system. It focuses on real, recency-aware benchmarks so recommendations better reflect current model performance. whichllm is especially helpful for developers, AI hobbyists, and researchers comparing local inference options across NVIDIA, AMD, Apple Silicon, and CPU-only environments. ... -
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PicoLM
Run a 1-billion parameter LLM on a $10 board with 256MB RAM
PicoLM is an open-source inference framework designed to run large language models on extremely constrained hardware environments such as inexpensive single-board computers and embedded systems. The project focuses on enabling efficient local inference by optimizing memory usage, computation, and system dependencies so that relatively large models can operate on devices with minimal RAM. It is written primarily in C and designed with a minimalist architecture that removes unnecessary dependencies and external libraries. ... -
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uzu
A high-performance inference engine for AI models
uzu is a high-performance inference engine designed to run artificial intelligence models efficiently on Apple Silicon hardware. Written primarily in Rust and leveraging Apple’s Metal framework, the project focuses on maximizing performance when executing large language models and other AI workloads on devices such as Mac computers with M-series chips. The engine implements a hybrid architecture in which model layers can be executed either as custom GPU kernels or through Apple’s MPSGraph API, allowing it to balance performance and compatibility depending on the workload. ... -
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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. ... -
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PowerInfer
High-speed Large Language Model Serving for Local Deployment
...This hybrid execution strategy significantly reduces memory bottlenecks and improves overall inference speed. 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.
