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Shares tags: deploy, hardware & accelerators, cpu-only optimizers
AI Tool
Optimize Your AI Models with Microsoft Olive
Effortless model compression and deployment for CPUs, GPUs, and NPUs.
Unlock significant cost savings with advanced mixed-precision and quantization techniques for large language models.Seamlessly integrate with a wide range of hardware and frameworks, ensuring optimal performance across diverse deployment environments.Experience a user-friendly interface that simplifies model optimization, making it accessible for both novices and experts.
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DeployHardware & AcceleratorsCPU-only Optimizers
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overview
What is Microsoft Olive?
Microsoft Olive is a powerful model compression and compilation pipeline designed to enhance the efficiency of AI models. It targets a variety of hardware platforms, including CPUs, GPUs, and NPUs, helping developers deploy models with ease.
- Supports ONNX models for broader compatibility.
- Optimizes for both cost and performance.
- Streamlines the deployment process.
features
Key Features of Microsoft Olive
Olive offers a plethora of features aimed at improving model performance while simplifying the optimization process. With new updates in mixed-precision and quantization, users can expect enhanced efficiency and reduced operational costs.
- Selective mixed precision and blockwise RTN quantization.
- Native GPTQ implementation for large language models.
- User-friendly CLI commands for optimization.
use_cases
Use Cases for Microsoft Olive
Olive is invaluable for AI model developers and machine learning engineers seeking to enhance model deployment across various hardware platforms. Whether you're working in the cloud or on edge devices, Olive simplifies the optimization lifecycle.
- Optimize and deploy AI models efficiently.
- Design models applicable to diverse hardware targets.
- Facilitate collaboration with frameworks like Hugging Face.
Frequently Asked Questions
Who can benefit from using Microsoft Olive?
Microsoft Olive is designed for AI model developers, machine learning engineers, and organizations that want to optimize deployment of ONNX models across various hardware platforms.
What new features does Olive include?
Recent updates include selective mixed precision, blockwise RTN quantization, and a native GPTQ implementation, all geared towards improving model efficiency and lowering deployment costs.
How does Olive simplify the optimization process?
With enhancements such as a streamlined CLI command, improved Python APIs, and advanced tools for evaluation and packaging, Olive enables users to optimize and test models with minimal manual intervention.