Language Large Models (LLMs) are the current hot topic in the field of artificial intelligence. A good level of progress has already been made in a wide range of industries such as healthcare, finance, education, entertainment, etc. Well-known large language paradigms such as GPT, DALLE, and BERT perform extraordinary tasks and make life easier. While GPT-3 can complete codes, answer questions like humans, and generate short content in natural language, DALLE 2 can generate images that respond to a simple text description. These models are contributing to some of the huge shifts in artificial intelligence and machine learning and helping them move through a paradigm shift.
With the development of an increasing number of models comes the need for powerful servers to accommodate their computing, memory, and hardware acceleration requirements. To make these models super effective and efficient, they need to be able to run autonomously on consumer devices, which will increase their accessibility and availability and enable users to access powerful AI tools on their personal devices without needing an internet connection or relying on cloud servers. Recently, MLC-LLM was introduced, an open framework that brings LLM directly to a wide class of platforms such as CUDA, Vulkan, and Metal that also have GPU acceleration.
MLC LLM enables language models to be deployed natively on a wide variety of back-end devices, including CPUs, GPUs, and native applications. This means that any language model can run on local machines without the need for a server or cloud-based infrastructure. MLC LLM provides a production framework that allows developers to optimize model performance for their use cases, such as natural language processing (NLP) or computer vision. It can even be accelerated using local GPUs, making it possible to run complex models with high precision and speed on personal machines.
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Specific instructions for running LLMs and chatbots are provided natively on hardware for iPhone, Windows, Linux, Mac, and web browsers. For iPhone users, MLC LLM provides an iOS chat app that can be installed through the TestFlight page. The app requires at least 6GB of memory to run smoothly and has been tested on iPhone 14 Pro Max and iPhone 12 Pro. The text generation speed on the iOS app can be unstable at times and may run slow at first before recovering to the normal speed.
For Windows, Linux and Mac users, MLC LLM provides a command line interface (CLI) application to chat with the bot in the device. Before installing the CLI application, users must install some dependencies, including Conda, to manage the application and the latest Vulkan driver for NVIDIA GPU users on Windows and Linux. After installing the dependencies, users can follow the instructions to install the CLI app and start chatting with the bot. For web browser users, MLC LLM provides a companion project called WebLLM, which deploys models natively in browsers. Everything runs inside the browser with no server support and is accelerated with WebGPU.
In conclusion, MLC LLM is an amazing universal solution for deploying LLM locally on diverse hardware backgrounds and native applications. It’s a great choice for developers who want to build models that can run on a wide range of devices and hardware configurations.
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Tania Malhotra is a final year from University of Petroleum and Energy Studies, Dehradun, pursuing a BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is passionate about data science and has good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.