Researchers at the Weizmann Institute of Science, Intel Labs, and d-Matrix have developed algorithms that greatly enhance speculative decoding for large language models. This approach can increase token generation rates by up to 2.8 times without the need for specialized draft models. Speculative decoding relies on a 'draft' model to predict outputs, allowing larger models to improve efficiency without losing quality. While this method can double or triple performance, finding compatible draft models poses a challenge due to vocabulary matching requirements.
Speculative decoding offers a new way to increase token generation rates significantly, achieving up to 2.8 times faster performance while avoiding the need for specialized draft models.
By using a smaller 'draft' model to predict outputs, speculative decoding enables large models to speed up their token generation without sacrificing quality, creating a lossless process.
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