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 hold tremendous potential for addressing numerous real-world challenges%2C yet they typically demand significant computational resources and memory. Deploying LLMs onto a resource-limited hardware device with restricted memory capacity presents considerable challenges. Distributed computing emerges as a prevalent strategy to mitigate single-node memory constraints and expedite LLM inference performance. To reduce the hardware limitation burden%2C we proposed an efficient distributed inference optimization solution for LLMs on CPUs. We conduct experiments with the proposed solution on 5th Gen Intel Xeon Scalable Processors%2C and the result shows the time per output token for the LLM with 72B parameter is 140 ms%2Ftoken%2C much faster than the average human reading speed about 200ms per token.?quality=80&w=800)
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 is an important challenge in artificial intelligence. This paper introduces Distributed Speculative Inference (DSI)%2C a novel distributed inference algorithm that is provably faster than speculative inference (SI) [leviathan2023fast%2Cchen2023accelerating%2Cmiao2023specinfer] and traditional autoregressive inference (non-SI). Like other SI algorithms%2C DSI works on frozen LLMs%2C requiring no training or architectural modifications%2C and it preserves the target distribution. Prior studies on SI have demonstrated empirical speedups (compared to non-SI) but require fast and accurate drafters%2C which are often unavailable in practice. We identify a gap where SI can be slower than non-SI given slower or less accurate drafters. We close this gap by proving that DSI is faster than both SI and non-SI--given any drafters. DSI introduces a novel type of task parallelism called Speculation Parallelism (SP)%2C which orchestrates target and drafter instances to overlap in time%2C creating a new foundational tradeoff between computational resources and latency. DSI is not only faster than SI but also supports LLMs that cannot be accelerated with SI. Our simulations show speedups of off-the-shelf LLMs in realistic single-node settings where DSI is 1.29-1.92x faster than SI.?quality=80&w=800)
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