# LLM Quantization Optimization Tools

> Search results for `quantize LLMs to run on smaller GPUs` on awesome-repositories.com. 119 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/quantize-llms-to-run-on-smaller-gpus

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## Results

- [axolotl-ai-cloud/axolotl](https://awesome-repositories.com/repository/axolotl-ai-cloud-axolotl.md) (12,059 ⭐) — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies.

The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,
- [automatic1111/stable-diffusion-webui](https://awesome-repositories.com/repository/automatic1111-stable-diffusion-webui.md) (163,743 ⭐) — Stable Diffusion Web UI is a browser-based interface designed for managing text-to-image generation tasks. It provides a centralized dashboard for controlling generative processes, including native support for multi-stage model architectures to facilitate high-quality image refinement.

The platform distinguishes itself through granular control over the generation process, offering tools for precise parameter management and advanced prompt engineering. Users can customize generation styles and capabilities by integrating external model-extension formats, such as textual inversions, low-rank ad
- [iusztinpaul/hands-on-llms](https://awesome-repositories.com/repository/iusztinpaul-hands-on-llms.md) (3,419 ⭐) — 🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
- [sjtu-ipads/powerinfer](https://awesome-repositories.com/repository/sjtu-ipads-powerinfer.md) (9,568 ⭐) — PowerInfer is an inference engine and serving framework designed to run large language models on local hardware. It combines a hybrid CPU-GPU offloader, a quantization tool, and a sparse model optimizer to enable the execution of high-parameter models on consumer-grade devices.

The system distinguishes itself through neuron-activation-based offloading, using a predictor model to preload frequent neurons into VRAM while keeping rare neurons in system memory. This hybrid execution model balances workloads between the GPU and CPU based on input patterns to optimize memory access and increase tok
- [systran/faster-whisper](https://awesome-repositories.com/repository/systran-faster-whisper.md) (21,043 ⭐) — Faster-Whisper is a high-performance implementation of the Whisper speech-to-text model designed for efficient audio transcription. It provides an end-to-end processing pipeline that converts spoken audio into written text while maintaining lower memory consumption and faster execution speeds than standard implementations.

The project achieves its performance through a specialized inference engine that utilizes optimized kernels and weight quantization to reduce computational complexity. It supports large-scale operations by grouping audio segments into dynamic batches and filtering out non-s
- [geshan/laravel6-on-google-cloud-run](https://awesome-repositories.com/repository/geshan-laravel6-on-google-cloud-run.md) (25 ⭐) — Laravel 6 on Google cloud run for a demo
- [uraimo/run-on-arch-action](https://awesome-repositories.com/repository/uraimo-run-on-arch-action.md) (747 ⭐) — A Github Action that executes jobs/commands on non-x86 cpu architectures (ARMv6, ARMv7, aarch64, s390x, ppc64le, riscv64) via QEMU
- [bentoml/openllm](https://awesome-repositories.com/repository/bentoml-openllm.md) (12,115 ⭐) — OpenLLM is a framework for deploying, managing, and scaling open-source large language models
- [opennmt/ctranslate2](https://awesome-repositories.com/repository/opennmt-ctranslate2.md) (4,319 ⭐) — CTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models.

The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di
- [darkdriller/powertoys-run-localllm](https://awesome-repositories.com/repository/darkdriller-powertoys-run-localllm.md) (31 ⭐) — PowerToys Run plugin which will enable to use LLMs on Ollama endpoints locally.
- [artidoro/qlora](https://awesome-repositories.com/repository/artidoro-qlora.md) (10,929 ⭐) — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models.

The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM.

The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset
- [facebook/react-native](https://awesome-repositories.com/repository/facebook-react-native.md) (126,019 ⭐) — This project is a cross-platform mobile framework that enables the development of native iOS and Android applications from a single codebase. It utilizes a declarative component-based model where developers define user interfaces using a syntax extension that maps directly to underlying platform-native view primitives. By decoupling application logic from the host platform's main thread, the framework maintains a consistent native view hierarchy while ensuring that JavaScript execution remains independent of UI rendering.

The framework distinguishes itself through a robust bridge architecture
- [huggingface/text-generation-inference](https://awesome-repositories.com/repository/huggingface-text-generation-inference.md) (10,775 ⭐) — Text Generation Inference is a production-ready engine designed for the deployment and serving of large language models. It functions as a containerized runtime environment that manages model execution, scales across distributed hardware, and provides high-performance inference capabilities for demanding production environments.

The project distinguishes itself through advanced optimization techniques, including continuous batching to maximize hardware utilization and tensor parallelism to shard large models across multiple accelerator cards. It supports efficient inference through custom com
- [eugeneyan/open-llms](https://awesome-repositories.com/repository/eugeneyan-open-llms.md) (12,804 ⭐) — 📋 A list of open LLMs available for commercial use.
- [1panel-dev/1panel](https://awesome-repositories.com/repository/1panel-dev-1panel.md) (35,898 ⭐) — 1Panel is a centralized server management and container orchestration platform designed to simplify the administration of Linux-based infrastructure. It provides a unified web interface for managing containerized workloads, automating system maintenance, and configuring server resources. By acting as a comprehensive control plane, the platform streamlines the deployment of applications, databases, and web services while offering granular control over host system internals and security settings.

What distinguishes this platform is its integrated support for private artificial intelligence infr
- [zju-llms/foundations-of-llms](https://awesome-repositories.com/repository/zju-llms-foundations-of-llms.md) (15,771 ⭐) — Foundations-of-LLMs is an educational curriculum and technical resource designed to explain the mathematical and computational principles behind modern generative language models. It provides a structured guide for developers and practitioners to master the fundamental concepts, architectural designs, and training methodologies that enable these systems to function.

The project covers the core mechanisms of transformer-based sequence modeling, including self-attention, subword tokenization, and autoregressive generation. It details the technical frameworks used in natural language processing
- [allegroai/clearml](https://awesome-repositories.com/repository/allegroai-clearml.md) (6,733 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving.

The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
- [ggerganov/llama.cpp](https://awesome-repositories.com/repository/ggerganov-llama-cpp.md) (116,912 ⭐) — llama.cpp is a high-performance C++ inference engine and runtime for executing large language models locally across various hardware architectures. It provides the core components for local model execution, including a dedicated model quantizer for compressing weights into the GGUF format and a system for generating text embeddings for semantic search.

The project distinguishes itself through specialized memory and execution optimizations, such as block-wise weight quantization to reduce memory footprints and memory-mapped model loading. It supports structured text generation by using formal
- [zai-org/chatglm-6b](https://awesome-repositories.com/repository/zai-org-chatglm-6b.md) (41,039 ⭐) — ChatGLM-6B is a generative AI inference engine designed for local execution of transformer-based language models. It provides a comprehensive runtime environment that allows users to load and run pre-trained neural network weights directly on their own hardware, ensuring data privacy and independence from external cloud services.

The project distinguishes itself through a hardware-agnostic execution backend that supports deployment across diverse environments, including standard processors, Apple Silicon, and multi-GPU configurations. It incorporates advanced optimization techniques such as w
- [clearml/clearml](https://awesome-repositories.com/repository/clearml-clearml.md) (6,740 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts.

The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
- [answerdotai/llms-txt](https://awesome-repositories.com/repository/answerdotai-llms-txt.md) (2,442 ⭐) — The /llms.txt file, helping language models use your website
- [datalab-to/marker](https://awesome-repositories.com/repository/datalab-to-marker.md) (36,137 ⭐) — Marker is a comprehensive document processing platform designed to automate the conversion, extraction, and structuring of data from complex files. It functions as an orchestration engine that chains modular processing steps into versioned, reusable pipelines, allowing organizations to standardize document handling and automate repetitive business tasks at scale.

The platform distinguishes itself through its support for secure, private infrastructure deployment, enabling users to run containerized services within their own environments to maintain strict data privacy. It features specialized
- [yhhhli/apot_quantization](https://awesome-repositories.com/repository/yhhhli-apot-quantization.md) (0 ⭐)
- [paddlepaddle/paddledetection](https://awesome-repositories.com/repository/paddlepaddle-paddledetection.md) (14,243 ⭐) — PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks.

The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
- [datalab-to/surya](https://awesome-repositories.com/repository/datalab-to-surya.md) (20,889 ⭐) — Surya is a document processing platform designed to transform unstructured files into structured, machine-readable data. It provides a comprehensive suite of tools for text recognition, layout analysis, and reading order detection, enabling the conversion of PDFs and images into formats such as JSON, HTML, or markdown. The platform is built to handle complex document workflows, offering capabilities for data extraction, document segmentation, and automated form completion.

The platform distinguishes itself through a robust pipeline-based architecture that allows users to chain analysis tasks
- [wdndev/llm_interview_note](https://awesome-repositories.com/repository/wdndev-llm-interview-note.md) (12,438 ⭐) — This project is a comprehensive technical reference and educational resource focused on the lifecycle of large language models. It provides structured learning materials that cover the foundational mechanics of transformer architectures, the mathematical principles of attention mechanisms, and the engineering practices required for modern generative artificial intelligence.

The repository serves as a guide for both technical skill development and professional preparation, offering a curriculum that spans from model training and inference optimization to advanced alignment techniques. It detai
- [km1994/llms_interview_notes](https://awesome-repositories.com/repository/km1994-llms-interview-notes.md) (2,567 ⭐) — This repository is a collection of notes and resources focused on large language models (LLMs), specifically curated for interview preparation. It serves as a study guide covering the key concepts, architectures, and practical knowledge needed to discuss LLMs in a technical interview setting.

The material spans the fundamental topics relevant to understanding and working with LLMs, including their underlying mechanisms, training processes, and evaluation methods. The notes are organized to help readers build a structured understanding of the field, from foundational principles to more advance
- [timoxley/npm-run](https://awesome-repositories.com/repository/timoxley-npm-run.md) (187 ⭐) — Use npm-run to ensure you're using the same version of a package on the command-line and in package.json scripts.
- [graphiteeditor/graphite](https://awesome-repositories.com/repository/graphiteeditor-graphite.md) (24,258 ⭐) — Graphite is a node-based visual design environment that integrates vector illustration, raster image processing, and motion graphics generation into a single platform. It utilizes a functional reactive pipeline and a data-flow execution model to propagate state changes through a graph of interconnected nodes, allowing users to construct complex, automated design workflows.

The platform distinguishes itself through a context-aware evaluation engine that injects runtime metadata—such as coordinate data and loop indices—directly into the node graph. This enables the creation of procedural geomet
- [ggerganov/ggml](https://awesome-repositories.com/repository/ggerganov-ggml.md) (14,831 ⭐) — ggml is a low-level C++ tensor library and machine learning inference engine designed for performing mathematical operations on multi-dimensional arrays across diverse hardware platforms. It provides a foundational toolset for executing machine learning models and calculating mathematical gradients through an automatic differentiation library.

The project features a quantized tensor framework that converts floating-point weights into integer representations to reduce memory usage and increase inference speed. It utilizes a custom binary format for model serialization to ensure rapid loading a
- [aider-ai/aider](https://awesome-repositories.com/repository/aider-ai-aider.md) (46,305 ⭐) — Aider is a command-line interface tool that enables large language models to directly edit, refactor, and manage source code within a local repository. It functions as an AI-powered coding assistant that integrates into the developer workflow, allowing users to apply code changes through natural language prompts while maintaining repository context and version control.

The tool distinguishes itself through a specialized diff-based patching engine that parses model-generated search-and-replace blocks to modify specific file segments without rewriting entire files. It features a provider-agnost
- [tracel-ai/burn](https://awesome-repositories.com/repository/tracel-ai-burn.md) (15,474 ⭐) — Burn is a deep learning framework designed for building, training, and deploying neural networks using a modular architecture. As a machine learning library built in Rust, it provides a backend-agnostic computational engine that enables the execution of models across diverse hardware, including central processors, graphics processors, and web runtimes.

The framework distinguishes itself through a highly portable design that allows developers to maintain a single workflow for both training and inference across heterogeneous environments. It incorporates advanced optimization techniques such as
- [run-house/kubetorch](https://awesome-repositories.com/repository/run-house-kubetorch.md) (1,212 ⭐) — Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.
- [vllm-project/vllm](https://awesome-repositories.com/repository/vllm-project-vllm.md) (83,048 ⭐) — vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token generation speed and memory efficiency, enabling both large-scale cloud deployments and local execution on personal hardware.

The project distinguishes itself through advanced memory management and request scheduling techniques, most notably its use of non-contiguous key-value cach
- [saschaseniuk/vite-plugin-llms](https://awesome-repositories.com/repository/saschaseniuk-vite-plugin-llms.md) (34 ⭐) — A Vite plugin that implements the llms.txt specification, enabling AI-optimized content alongside your routes. It automatically serves markdown files for LLM consumption and handles the llms.txt routing in development and production.
- [huggingface/transformers](https://awesome-repositories.com/repository/huggingface-transformers.md) (161,630 ⭐) — Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference.

The library features extensive support for model optimization and
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [jfairbank/run-elm](https://awesome-repositories.com/repository/jfairbank-run-elm.md) (53 ⭐) — Run Elm code from the command line
- [docker/genai-stack](https://awesome-repositories.com/repository/docker-genai-stack.md) (5,333 ⭐) — This project is a containerized development stack and application framework for building retrieval-augmented generation systems. It provides a dockerized AI sandbox that integrates local model runtimes, knowledge graphs, and vector stores to enable the creation of contextual chatbots.

The stack is distinguished by its graph-based vector store, which combines structured knowledge graphs with vector indices for both semantic and structural data retrieval. It allows for local model hosting with CPU or GPU acceleration, enabling generative tasks without reliance on external cloud APIs.

The frame
- [verl-project/verl](https://awesome-repositories.com/repository/verl-project-verl.md) (22,000 ⭐) — This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement.

The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This
- [sindresorhus/run-electron](https://awesome-repositories.com/repository/sindresorhus-run-electron.md) (204 ⭐) — Run Electron without all the junk terminal output
- [googlecloudplatform/cloud-run-mcp](https://awesome-repositories.com/repository/googlecloudplatform-cloud-run-mcp.md) (618 ⭐) — MCP server to deploy apps to Cloud Run
- [ant-design/ant-design](https://awesome-repositories.com/repository/ant-design-ant-design.md) (98,362 ⭐) — Ant Design is an enterprise-grade component library and design system framework built for developing complex, data-heavy web applications. It provides a comprehensive collection of pre-built, state-driven interface elements that map data properties to rendered components, ensuring consistent interaction patterns and visual language across large-scale projects.

The library distinguishes itself through a robust styling architecture that utilizes design tokens and hierarchical configuration providers to propagate global settings like themes, locale, and layout direction. By employing component-l
- [setzer22/llama-rs](https://awesome-repositories.com/repository/setzer22-llama-rs.md) (6,150 ⭐) — llama-rs is a local large language model inference engine implemented in Rust. It enables the execution of model computations on local hardware to generate text responses from user prompts.

The project utilizes Rust-based tensor operations and direct-memory model mapping to handle high-performance linear algebra and efficient weight loading. It incorporates weight quantization to reduce the memory footprint of models by converting high-precision weights into smaller formats.

The system includes a command-line interface for interactive chat sessions and one-off prompts, along with file-backed
- [evancz/elm-format-on-save](https://awesome-repositories.com/repository/evancz-elm-format-on-save.md) (22 ⭐) — Sublime Text plugin to run elm-format on save
- [jetbrains/kotlin](https://awesome-repositories.com/repository/jetbrains-kotlin.md) (52,880 ⭐) — Kotlin is a statically typed, general-purpose programming language designed for type safety and concise syntax. It functions as a cross-platform development toolkit that enables the sharing of business logic across mobile, web, and server-side environments by compiling a unified intermediate representation into platform-specific machine code, bytecode, or source code.

The project distinguishes itself through a multi-target build orchestration model that manages complex compilation units and hierarchical source sets. Developers can define common interface logic that is satisfied by platform-sp
- [meta-pytorch/gpt-fast](https://awesome-repositories.com/repository/meta-pytorch-gpt-fast.md) (6,223 ⭐) — gpt-fast is a PyTorch transformer inference engine designed for text generation using a native tensor library implementation. It provides a runtime for executing large language models without the need for external C++ extensions.

The project implements speculative decoding to accelerate generation by using a small draft model for token prediction and a larger model for verification. It further optimizes performance through a compiled prefill stage and a multi-GPU tensor parallelism library that shards linear layers across multiple graphics processing units.

Memory efficiency is managed throu
- [akryum/monorepo-run](https://awesome-repositories.com/repository/akryum-monorepo-run.md) (184 ⭐) — Run scripts in monorepo with colors, streaming and separated panes
- [modelscope/ms-swift](https://awesome-repositories.com/repository/modelscope-ms-swift.md) (14,597 ⭐) — This project is a comprehensive toolkit designed for the full lifecycle management of large language and multimodal models. It functions as a unified orchestrator that handles the entire development process, ranging from dataset preparation and supervised fine-tuning to advanced reinforcement learning alignment and production-ready inference deployment.

The platform distinguishes itself through a specialized reinforcement learning library that supports complex optimization algorithms, including group relative policy optimization and leave-one-out techniques, to improve model instruction-follo
- [denoland/deno](https://awesome-repositories.com/repository/denoland-deno.md) (107,110 ⭐) — Deno is a high-performance runtime for JavaScript and TypeScript that prioritizes security and developer productivity. Built on the V8 engine, it provides a secure execution environment that enforces a default-deny security model, requiring explicit user authorization for access to system resources like the file system, network, and environment variables. The runtime natively supports modern web-standard APIs, ensuring consistent behavior and portability across different environments.

What distinguishes Deno is its integrated approach to the software development lifecycle. It bundles essentia