GGUF and ONNX in Enterprise: Quantized Model Formats for Cost-Effective Deployment

August 1, 2026Open Access

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Key Points

This article aims to explore the use of GGUF and ONNX formats for deploying quantized machine learning models in business settings.
Evaluation of quantized model formats GGUF and ONNX.
Analysis of cost-effectiveness regarding deployment in enterprise environments.
Comparison of performance metrics associated with these formats.
GGUF shows a decrease in deployment costs by 30% compared to traditional formats.
ONNX demonstrates improved efficiency with less computing power needed by 25%.
Both formats enhance model performance without sacrificing accuracy.

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Research article demonstrates cost-effective deployment of quantized models in enterprises using GGUF and ONNX.

Research article: GGUF and ONNX in Enterprise: Quantized Model Formats for Cost-Effective Deployment

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Ивченко et al. (2026) studied this question.

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