Audience
Professionals and developers interested in a solution to optimize their AI models
About NetsPresso
NetsPresso is a hardware-aware AI model optimization platform. NetsPresso powers on-device AI across industries, and it's the ultimate platform for hardware-aware AI model development. Lightweight models of LLaMA and Vicuna enable efficient text generation. BK-SDM is a lightweight version of Stable Diffusion models. VLMs combine visual data with natural language understanding. NetsPresso resolves Cloud and server-based AI solutions-related issues, such as limited network, excessive cost, and privacy breaches. NetsPresso is an automatic model compression platform that downsizes computer vision models to a size small enough to be deployed independently on the smaller edge and low-specification devices. Optimization of target models being key, the platform combines a variety of compression methods which enables it to downsize AI models without causing performance degradation.
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Integrations
Company Information
Nota AI
Founded: 2015
South Korea
www.nota.ai/netspresso
Videos and Screen Captures
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Product Details
Platforms Supported
Cloud
On-Premises
Training
Documentation
Videos
Support
Phone Support
Online
NetsPresso Frequently Asked Questions
Q: What kinds of users and organization types does NetsPresso work with?
NetsPresso works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does NetsPresso support in their product?
NetsPresso supports these languages: English.
Q: What kind of support options does NetsPresso offer?
NetsPresso offers support via phone support and online.
Q: What other applications or services does NetsPresso integrate with?
NetsPresso integrates with: Arduino IDE, Arm MAP, Intel SceneScape, LaunchX, MQX RTOS, NVIDIA AI Enterprise, Python, Qualcomm AI Hub, and Raspberry Pi OS.
Q: What type of training does NetsPresso provide?
NetsPresso provides training in the form of documentation and videos.
