Multiple Dynamic Task-Specific AI Agents in transformers.js: Efficient Client-Side Inference with WebGPU with State and Context Preservation Across Reloads

[r00tmeister]

Feature request

Overview

I propose adding support in transformers.js for implementing multiple task-specific AI agents that load dynamically per page or task, utilizing WebGPU for client-side execution, while maintaining shared memory or context storage. This feature would enable web applications to efficiently leverage AI by making use of "chunking" models according to specific tasks or pages, ensuring that only the required model is loaded for each context. This approach optimizes both performance and resource usage by avoiding unnecessary model loads.

Key Challenges

One of the main challenges is state management and context preservation across model reloads. Since models may be dynamically loaded and unloaded, maintaining intermediate states, embeddings, or other contextual information becomes essential for continuity and accuracy.

Proposed Solution: ContextDB for Context Storage and Shared Memory

To manage state effectively, I suggest using a ContextDB for external context storage, serialization, and shared memory. ContextDB would provide a structured, reliable storage solution for persisting and retrieving the AI model's state.

Serialization in Context Management

In this setup, serialization plays a crucial role. Serialization refers to converting complex data structures, such as model states or embeddings, into a format that can be easily stored, transmitted, and later reconstructed. By serializing these states, ContextDB can ensure that the state of an AI model (or its parts) can be persistently stored and restored as needed, without any loss of critical information.

This setup would support the following:

• Task-specific model loading with minimal overhead
• Optimized memory and resource usage by selectively loading models
• State preservation across dynamic model reloads, allowing seamless task transitions

With this architecture, transformers.js could deliver robust, task-oriented AI capabilities on the client side.

Motivation:

Currently, loading large AI models (such as 3B+ parameter models) into the browser results in significant memory consumption and resource overhead, even when the model is not actively used for every task. While client-side execution can reduce latency by avoiding server calls, it still comes with the challenge of managing these massive models in resource-constrained environments, especially on the client.

The proposed feature would allow web applications to load smaller, task-specific AI agents dynamically, based on the current page or task. By only loading the models required for each context, we can significantly reduce the memory and processing overhead, providing a more optimized, low-latency user experience without overloading the client’s resources.

Key Benefits:

1. Efficient Resource Usage: Load only the necessary AI models per task/page, reducing memory usage and the strain on the client.
2. Improved Performance: Dynamic loading of smaller AI models tailored to specific tasks results in faster processing times and reduced resource contention.
3. Scalability: Support for multiple AI agents that can be used across different tasks, allowing task-specific models to be moved and executed more efficiently.
4. Optimized User Experience: With WebGPU’s low-latency and hardware acceleration, users will experience faster AI interactions without waiting for large models to load.

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Your contribution

I would be happy to contribute to the development of this feature by:

• Helping to design and implement the API for dynamic loading of AI agents.
• Contributing to the integration of WebGPU support to ensure efficient client-side execution.
• Writing tests and documentation to ensure smooth implementation and usage of the feature.

This feature could be implemented as a pipeline-like function for managing dynamic model loading and task-specific agent execution.