Neural Magic

Neural Magic

Unlocking the possibility of machine learning, by re-imagining how deep learning is done.

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Total Funding000k

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Revenues, earnings & profits over time
USD202120222023
Revenues000000000000
% growth-29 %81 %
EBITDA000000000000
Profit000000000000
EV000000000000
EV / revenue00.0x00.0x00.0x
EV / EBITDA00.0x00.0x00.0x
R&D budget000000000000

Source: Dealroom estimates

More about Neural Magic
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Neural Magic operates in the artificial intelligence (AI) and machine learning (ML) sector, focusing on optimizing neural network models for deployment on central processing units (CPUs) instead of graphics processing units (GPUs). The company provides a suite of tools and platforms, including the Neural Magic DeepSparse Engine, SparseZoo, and SparseML, which enable users to achieve GPU-class performance on CPUs by leveraging sparsity-aware techniques. These tools are particularly beneficial for natural language processing (NLP) and computer vision applications, allowing for faster and more efficient model inference.

Neural Magic serves a diverse range of clients, including AI researchers, data scientists, and enterprises looking to deploy high-performance AI models without the need for expensive GPU hardware. The company operates in the broader AI and ML market, which is rapidly growing as more industries adopt AI technologies for various applications.

The business model of Neural Magic revolves around providing both open-source and commercial solutions. They offer free access to their open-source tools, encouraging widespread adoption and community contributions. Additionally, they generate revenue through premium features, enterprise support, and custom solutions tailored to specific client needs.

Neural Magic makes money by offering subscription-based services, enterprise-grade solutions, and consulting services. Their focus on CPU optimization allows clients to reduce hardware costs while maintaining high performance, making their offerings attractive to cost-conscious organizations.

Keywords: AI optimization, CPU inference, neural networks, sparsity-aware, NLP models, computer vision, DeepSparse Engine, SparseZoo, SparseML, model pruning.

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