What is Large Scale Generative AI?
Updated: November 18, 2024
Summary
Running generative AI algorithms at scale poses significant challenges due to the exponential growth in model size, data size, and demand over time. The model size has evolved from thousands to billions of parameters, necessitating advanced hardware for efficient training. Strategies like batch-based and cache-based systems are utilized to manage the load efficiently, with techniques such as model distillation and the student-teacher approach being employed to compress models and enhance performance.
TABLE OF CONTENTS
Challenges of Running Generative AI Algorithms at Scale
Exponential Growth in Model Size
Exponential Growth in Data Size
Exponential Growth in Demand
Scaling Generative AI Algorithms Across GPUs
Batch-Based Generative AI System
Cache-Based Generative AI System
Agentic Architecture Approach
Model Distillation Technique
Student-Teacher Approach
Challenges of Running Generative AI Algorithms at Scale
Running generative AI algorithms at scale can be very challenging, overwhelming, and costly due to exponential growth in model size, data size, and demand over time.
Exponential Growth in Model Size
Model size has grown from thousands to millions and now billions of parameters, requiring advanced hardware to run and train these large algorithms.
Exponential Growth in Data Size
Data size is also growing exponentially, with synthetic data overtaking real-world data. Algorithms can process a billion times more data in a month than a human can read in a year.
Exponential Growth in Demand
The demand for models like GPT has increased significantly, with millions of users within days of release. This high demand requires an unfathomable compute scale to run specialized models effectively.
Scaling Generative AI Algorithms Across GPUs
Generative AI algorithms can be scaled across hundreds of GPUs, straining both the system and underlying hardware. Different strategies like batch-based and cache-based systems are used to manage the load efficiently.
Batch-Based Generative AI System
In a batch-based system, dynamic fill-in-the-blank sentences from large language models are stored on a Content Delivery Network (CDN) and personalized information is inserted to provide a personalized experience.
Cache-Based Generative AI System
A cache-based system stores content on servers globally and generates content for common cases, while on-demand content generation serves less common requests, striking a balance between pre-generated and on-demand content.
Agentic Architecture Approach
Agentic architecture involves breaking down complex models into smaller specialized models that communicate with each other. These smaller models require smaller footprints and can be scaled across different GPUs.
Model Distillation Technique
Model distillation involves compressing a model into a smaller footprint through techniques like quantization, enhancing performance and adaptability to specific tasks.
Student-Teacher Approach
The student-teacher approach involves training a smaller model by learning from a larger teacher model, creating new skills or refining existing ones in a supervised manner.
FAQ
Q: What challenges are associated with running generative AI algorithms at scale?
A: Running generative AI algorithms at scale can be challenging due to exponential growth in model size, data size, and demand over time.
Q: How has model size evolved in generative AI algorithms?
A: Model size has grown from thousands to millions and now billions of parameters.
Q: What is the impact of the growing data size on generative AI algorithms?
A: Data size is also growing exponentially, with synthetic data overtaking real-world data.
Q: How does the demand for models like GPT impact the computational requirements?
A: The demand for models like GPT has increased significantly, requiring an unfathomable compute scale to run effectively.
Q: What are some strategies used to manage the load efficiently in generative AI algorithms?
A: Batch-based and cache-based systems are used to manage the load efficiently.
Q: What is the difference between a batch-based and cache-based system in generative AI algorithms?
A: In a batch-based system, dynamic fill-in-the-blank sentences are stored on a Content Delivery Network (CDN) and personalized information is inserted. In a cache-based system, content is pre-generated for common cases while on-demand content generation serves less common requests.
Q: What is agentic architecture in the context of generative AI algorithms?
A: Agentic architecture involves breaking down complex models into smaller specialized models that communicate with each other.
Q: What is model distillation and how does it benefit generative AI algorithms?
A: Model distillation involves compressing a model into a smaller footprint through techniques like quantization, enhancing performance and adaptability to specific tasks.
Q: What is the student-teacher approach in generative AI algorithms?
A: The student-teacher approach involves training a smaller model by learning from a larger teacher model, creating new skills or refining existing ones in a supervised manner.
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