As large language models (LLMs) become increasingly embedded in chatbots, virtual assistants, translation services, coding tools and other AI-powered applications, delivering responses quickly and efficiently has become a growing challenge. Because these models generate text one token at a time, inference can be slow and computationally expensive, particularly for larger models. While speculative decoding has emerged as a promising approach to accelerate inference, many existing methods either require additional model training or struggle to perform consistently across different hardware platforms.As large language models (LLMs) become increasingly embedded in chatbots, virtual assistants, translation services, coding tools and other AI-powered applications, delivering responses quickly and efficiently has become a growing challenge. Because these models generate text one token at a time, inference can be slow and computationally expensive, particularly for larger models. While speculative decoding has emerged as a promising approach to accelerate inference, many existing methods either require additional model training or struggle to perform consistently across different hardware platforms.Hardware[#item_full_content]