Large Language Models (LLMs) on the Edge: Because who needs the Cloud Anyway?

6/2/24

Editorial team at Bits with Brains

Large language models (LLMs) such as OpenAI's GPT-4 and Meta's LLaMA, are revolutionizing natural language processing and generation everywhere, including the Edge.

Large language models (LLMs) such as OpenAI's GPT-4 and Meta's LLaMA, are revolutionizing natural language processing and generation. However, their deployment has traditionally been limited to powerful cloud servers due to their substantial computational and memory requirements. The shift towards running these LLMs on edge devices—such as smartphones, IoT devices, and autonomous systems—promises to enhance real-time processing, reduce latency, and improve data privacy.

This transition is driven by the need for immediate, localized AI responses without relying on constant cloud connectivity.

We already have numerous early examples of LLMs on edge devices, ranging from Apple’s Siri and Google’s Assistant to Huawei’s HiAI, Nvidia’s Jarvis and Qualcomm’s AI Stack.

One essential ingredient is multi-modality. That is, the capability of artificial intelligence systems to process, understand, and generate outputs from multiple types of data, such as text, images, audio, and video, simultaneously. This approach mimics human cognition by combining different types of sensory data to create a more comprehensive understanding of the environment. The ability to process multiple data types concurrently on edge devices is a relatively new capability and enhances the versatility and robustness of AI applications, making them more effective and user-friendly.

Another is LLM optimization and quantization. Techniques such as weight quantization, pruning, and knowledge distillation are crucial in reducing the model size and computational load without sacrificing performance. For example, MIT's TinyChat employs a combination of 4-bit weight and 8-bit activation quantization to run LLMs efficiently on devices with limited memory and processing power. The model is designed to be highly efficient, requiring only around 100MB of storage space. Similarly, NVIDIA's IGX Orin Developer Kit leverages 4-bit precision to fit large models like LLaMA 2 70B into edge-compatible hardware.

This is a major area of focus and excitement within AI research because of the numerous potential use cases. Here are just a few examples:

Healthcare: Edge AI can revolutionize patient monitoring and diagnostics by processing data from wearable devices in real-time, enabling immediate medical interventions and personalized treatment plans.
Autonomous Vehicles: Real-time processing of sensor data for navigation, obstacle detection, and decision-making is critical for the safe operation of autonomous vehicles.
Smart Cities: Edge AI can enhance urban management by analyzing data from various sensors to optimize traffic flow, manage energy consumption, and improve public safety.
Industrial IoT: In manufacturing, edge AI can monitor equipment health, predict maintenance needs, and optimize production processes, reducing downtime and increasing efficiency.
Retail: Edge AI can enhance customer experiences through personalized recommendations, real-time inventory management, and automated checkout systems.

One of the primary benefits is enhanced data privacy, as sensitive information can be processed locally without being transmitted to the cloud. This is particularly important in sectors like healthcare and finance. Additionally, edge AI reduces latency, providing faster responses crucial for applications like autonomous driving and real-time analytics.

However, the effective deployment of edge AI still faces significant challenges. One is the limited computational and memory resources of edge devices necessitate advanced optimization techniques to ensure efficient model performance. Another is the heterogeneity of edge devices, each with different capabilities and constraints, complicating the development of universally compatible AI solutions. Security is also of critical concern, as edge devices are often more vulnerable to cyberattacks compared to centralized cloud servers.

While all of these are still areas of active research, progress is exceedingly, and will drive the next wave of digital transformation.

Sources:

[1] https://hanlab.mit.edu/blog/tinychat

[2] https://embeddedcomputing.com/technology/ai-machine-learning/how-multimodal-ai-will-shape-the-edge

[3] https://arxiv.org/html/2405.07140v1

[4] https://www.youtube.com/watch?v=jabz14Y1LVA

[5] https://www.edgecortix.com/en/blog/multimodal-generative-ai-on-energy-efficient-edge-processors

[6] https://arxiv.org/abs/2405.07140

[7] https://developer.nvidia.com/blog/deploy-large-language-models-at-the-edge-with-nvidia-igx-orin-developer-kit/

[8] https://www.edge-ai-vision.com/2024/05/technologies-driving-enhanced-on-device-generative-ai-experiences-multimodal-generative-ai/

[9] https://www.qualcomm.com/news/onq/2023/12/optimizing-generative-ai-for-edge-devices

[10] https://www.edgeimpulse.com/blog/llm-knowledge-distillation-gpt-4o/