Advances to low-bit quantization enable LLMs on edge devices
Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these devices supports advanced AI and real-time services, but their massive size, with hundreds of millions of parameters, requires significant memory and computational power, limiting widespread adoption. Low-bit quantization, a technique that compresses models and reduces memory demands, offers a solution by enabling more efficient operation.
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