Distributed inference. Distributed inference can fall into three brackets: Loading an entire model onto each GPU and sending chunks of a batch through each ...

On distributed setups, you can run inference across multiple GPUs with Accelerate or PyTorch Distributed, which is useful for generating with multiple prompts ...

vLLM supports distributed tensor-parallel and pipeline-parallel inference and serving. Currently, we support Megatron-LM's tensor parallel algorithm. We manage ...

This functionality enables LocalAI to distribute inference requests across multiple worker nodes, improving efficiency and performance.

Distributed inference means use multiple devices for prediction. If data parallel or integrated save is used in training, the method of distributed inference is ...

13 дек. 2023 г. · In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We ...

8 июл. 2023 г. · This page provides an overview of distributed-inference techniques. This topic is still very into development, but it looks like it could be an ...

WHY THIS PROJECT? This project aims to demonstrate an approach to designing cross-language and distributed pipeline in deep learning/machine learning domain.

vLLM supports distributed tensor-parallel inference and serving. Currently, we support Megatron-LM's tensor parallel algorithm. We manage the distributed ...

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots.