Deepcompressor

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deepcompressor is a Coding & Development tool that provides a model compression toolbox for large language models and diffusion models. It supports various quantization algorithms to optimize AI models for efficiency.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is deepcompressor?

DeepCompressor is an open-source model compression toolbox designed for large language models (LLMs) and diffusion models, built on PyTorch. It offers a range of post-training quantization algorithms, including weight-only quantization (AWQ, GPTQ) and weight-activation quantization (SmoothQuant, QoQ, SVDQuant). The tool supports various integer and floating-point data types within 8 bits, such as INT8, INT4, and FP4_E2M1. DeepCompressor also provides examples for integration with inference libraries like TinyChat, QServe, and Nunchaku, enabling efficient deployment of quantized models. Its SVDQuant algorithm, for instance, significantly reduces memory usage and accelerates diffusion models like SDXL and FLUX.1, while QServe enhances LLM serving throughput on GPUs.

Best used for

Ideal for developers and machine learning engineers who need to optimize large language models and diffusion models, accelerate AI inference, and reduce memory footprint. Especially valuable for deploying efficient AI models on various hardware, including GPUs, and integrating with existing inference systems.

Common actions

compress AI models

quantize large language models

optimize diffusion models

accelerate AI inference

reduce model memory

open-sourcecollaborationworkflowsautomated workflowgithub copilotface swappinglow-code/no-code"AI Agents"deepfake

Capabilities

Key features

Model compression toolbox
Large language model quantization
Diffusion model quantization
Weight-only quantization
Weight-activation quantization
KV-cache quantization
Integrate with inference libraries

Target Audience

developermachine learning engineerresearcher

Integrations

pytorchnvidia-tensorrt-llm

Pricing & Plans

Open Source

Free

FAQs

What types of quantization does DeepCompressor support?

DeepCompressor supports various post-training quantization methods, including weight-only quantization (AWQ, GPTQ) and weight-activation quantization (SmoothQuant, QoQ, SVDQuant). It can quantize models to integer and floating-point data types within 8 bits, such as INT8, INT4, and FP4_E2M1.

Which models can be optimized using DeepCompressor?

DeepCompressor is specifically designed for large language models (LLMs) and diffusion models. It has been tested and shown effective on models like Llama-3, Qwen1.5, SDXL, PixArt-Sigma, and FLUX.1, significantly improving their efficiency and reducing resource requirements.

How does DeepCompressor improve inference performance?

DeepCompressor improves inference performance by reducing model size and computational requirements through quantization. For example, its QServe system can achieve 1.2x-3.5x higher throughput for LLMs compared to TensorRT-LLM, and SVDQuant accelerates diffusion models by up to 3.0x.

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