Comparing the Deployment of Large Language Models: A Guide #AIdeployment

The article discusses the deployment landscape of Large Language Models (LLMs) and the tools available for deploying and managing them in production settings. While tutorials on training LLMs are abundant, resources on deploying and monitoring them are scarce. The article covers tools like VLLM, LLAMA.cpp, and TGI, highlighting their unique approaches to LLM deployment.

VLLM is a fast and easy-to-use library for LLM inference and serving, offering features like PagedAttention, Tensor Parallelism support, and ease of use for production settings. TGI is a Rust and Python inference framework used by HuggingFace, with features similar to VLLM but with a focus on Apple silicon. LLAMA.cpp is a C/C++ implementation optimized for diverse hardware configurations, offering features like splitting workloads between GPU and CPU.

The article provides examples of using VLLM for offline batched inference and deploying a compatible OpenAi Server. It also explains how to deploy models with TGI and LLAMA.cpp, highlighting their unique features and performance capabilities. While LLAMA.cpp excels in hardware utilization, it may lag behind competitors in raw performance, making it suitable for projects with limited vRAM.

Overall, the tools discussed in the article offer a flexible foundation for deploying, serving, and fine-tuning LLMs in various settings, whether in the cloud or offline. These tools cater to different needs and provide opportunities for experimentation and staying ahead in the rapidly evolving field of AI and LLMs.

Source link

Source link: https://medium.com/@ahmed.mimilahlou/navigating-the-deployment-landscape-of-large-language-models-a-comparative-guide-17f93190da29?source=rss——llm-5