Most advanced machine learning models, especially those achieving state-of-the-art results, require significant computational resources such as GPUs and TPUs. Deploying large models in resource-constrained environments like edge devices, mobile platforms, or other low-power hardware restricts the application of machine learning to cloud-based services or data centers, limiting real-time applications and increasing latency. Access to high-performance hardware is expensive, both in terms of acquisition and operation, which creates a barrier for smaller organizations and individuals who want to leverage machine learning. Researchers address the challenge of large models’ computational resource intensity. Current methods for running large language models typically rely on powerful hardware or cloud-based solutions, which can be costly and inaccessible for many applications. Existing solutions often struggle with optimizing performance on commodity hardware due to their heavy computational […]
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