Inference with Gemma using Dataflow and vLLM
Large language models (LLMs) like Gemma are powerful and versatile. They can translate languages, write different kinds of text content, and answer your questions in an informative way. However, deploying these LLMs to production, especially for streaming use cases, can be a significant challenge.
This blog post will explore how to use two state-of-the-art tools, vLLM and Dataflow, to efficiently deploy LLMs at scale with minimal code. First, we will lay out how vLLM uses continuous batching to serve LLMs more efficiently. Second, we will describe how Dataflow's model manager makes deploying vLLM and other large model frameworks simple.
vLLM is an open-source library specifically designed for high-throughput and low-latency LLM inference. It optimizes the serving of LLMs by employing several specialized techniques, including continuous batching.
To understand how continuous batching works, let's first look at how models traditionally batch inputs. GPUs excel at parallel processing, where multiple computations are performed simultaneously. Batching allows the GPU to utilize all of its available cores to work on an entire batch of data at once, rather than processing each input individually. This significantly speeds up the inference process; often, performing inference on 8 input records at once uses similar resources as performing inference on a single record.
