[[{“value”:”Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry.
Earlier this year, we published and open sourced Recurrent Drafter (ReDrafter), a novel approach to speculative decoding that achieves state of the art…”}]] [[{“value”:”Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry.
Earlier this year, we published and open sourced Recurrent Drafter (ReDrafter), a novel approach to speculative decoding that achieves state of the art…”}]] Read More