NanoFlow - A service framework for optimizing inference throughput for large language models

What is NanoFlow?

NanoFlow is a high-performance service framework designed for large language models (LLMs) to improve inference throughput. It optimizes the inference process by using computation, memory, and network resources in parallel within a single device. This parallel processing mechanism allows NanoFlow to handle more requests simultaneously while ensuring fast response times, significantly improving overall system performance and user experience.

NanoFlow's main functions

• Improve inference throughputNanoFlow's core objective is to maximize the model's inference throughput, which means increasing the number of tokens processed per second while ensuring reasonable latency.
• Intra-device parallelismNanoFlow improves resource utilization by using operation-level pipelines and execution unit scheduling to process different operations in parallel within a single device.
• Automated parameter searchNanoFlow uses an automated parameter search algorithm to adapt to different models, reducing manual intervention and simplifying the model deployment and optimization process.
• Global batch scheduling: Manage requests through a global batch scheduler and select the optimal batch size to improve computational efficiency.
• Operational-level parallelism engine: Divide requests into smaller batches (nano-batches) and assign them to different execution units to achieve operation-level parallelism.

NanoFlow's technical principles

• Global batch schedulerImprove computational efficiency by managing requests and selecting the optimal intensive batch size.
• In-device parallelism engine: Requests are divided into smaller batches (called nano-batches) and assigned to different execution units to achieve operation-level parallelism.
• KV Cache Manager: Optimize memory usage by predicting peak memory usage and promptly offloading completed key-value cache requests to lower-level storage.

NanoFlow project address

• GitHub repository addresshttps://github.com/efeslab/Nanoflow
• arXiv technical paper:https://arxiv.org/pdf/2408.12757

How to use NanoFlow

• Visit the GitHub repositoryVisit the GitHub repository to get the latest version of NanoFlow and related documentation.
• Reading documentsIn the GitHub repository, view the README file and related documentation.
• Mounting FrameInstall using specific commands or through a package manager.
• Running ExampleRun the sample code to ensure NanoFlow works correctly.
• Customization and ExtensionNanoFlow can be customized and extended according to your needs.

NanoFlow application scenarios