Artificial Intelligence on the Edge: Benefits, Challenges and Available Tools
The adoption of Artificial Intelligence (AI) models executed locally on one's own computers is becoming increasingly common, offering significant advantages in terms of security and privacy. Running AI on the edge allows keeping sensitive data within one’s own environment, reducing the risks associated with transmitting information over external networks. However, this approach also presents some challenges, including the need for powerful hardware and adequate storage space.
The adoption of Artificial Intelligence (AI) models executed locally on one’s own computers is becoming increasingly common, offering significant advantages in terms of security and privacy. Running AI on the edge allows keeping sensitive data within one’s own environment, reducing the risks associated with transmitting information over external networks. However, this approach also presents some challenges, including the need for powerful hardware and adequate storage space.
Benefits of Edge AI
- Data Security: Executing AI models locally ensures that data remains within your system, giving you full control over sensitive information. This is particularly important for businesses and professionals handling confidential data.
- Personalization: Local implementation allows for greater personalization of AI models, tailoring them to the specific needs of the user or organization.
Challenges of Edge AI
- Hardware Requirements: Running advanced AI models requires a dedicated and performant GPU. GPUs like NVIDIA RTX 30 or 40 series or AMD Radeon’s latest generation are often necessary for managing and efficiently handling these workloads. It is also advisable to have at least 16 GB of RAM to ensure smooth performance.
- Storage Space: AI models can occupy several gigabytes of disk space. For example, models like Llama3 may require up to 11 GB. Therefore, it is essential to ensure you have sufficient SSD storage for installing and running the desired models.
Available Tools and Models
- NVIDIA ChatRTX: A demo application that allows customizing a large language model (LLM) GPT connected to your own content, such as documents, notes, images, or other data. It uses RAG (Retrieval-Augmented Generation) technology and RTX acceleration to provide relevant responses quickly and securely, running everything locally on Windows PCs or workstations with an RTX GPU.
- Llama3 by Meta: A family of large language models developed by Meta, available in various sizes, including 8B and 70B parameters. These models are optimized for dialogue use and can be run locally on adequate hardware. For example, the Llama3-ChatQA-1.5-70B model was developed to excel in conversational question answering and retrieval-augmented generation.
- Ollama: An open-source framework that facilitates running models like Llama3 on local systems. Ollama allows downloading and managing various AI models, providing an interface for chatting directly with the artificial intelligence on your PC. It is compatible with multiple LLMs and supports execution on hardware with adequate specifications.