Grant #240273 to Web AI Fund
Web LLM Accelerator Framework
Rejected
Grant #240273
ai
llm
Submitted by Derrick Cerf
Feb 26, 2025
Request Details
Project Summary
WebLLM Accelerator is an open-source toolkit that enables efficient deployment and operation of smaller language models directly in the browser using WebGPU, eliminating cloud dependencies while preserving user privacy.
Why This Matters
Current browser-based AI implementations face significant performance bottlenecks and memory constraints. My framework will address these challenges by:
- Optimizing model quantization specifically for WebGPU constraints
- Implementing progressive loading for larger models
- Providing cross-framework bindings for React, Vue, and Svelte
Technical Implementation Plan ( what I have done so far )
1. Develop a WebGPU-optimized inference engine for 1-3B parameter models
2. Create adaptive quantization techniques that respond to device capabilities
3. Build a prompt engineering toolkit that maximizes performance from smaller models
4. Provide simple APIs that abstract WebGPU complexity from developers
Initial Milestones (3-month timeline)
- Month 1: WebGPU kernel optimization and model compression toolkit
- Month 2: Progressive loading system and framework integrations
- Month 3: Documentation, demos, and educational resources
Why This Will Succeed
The project directly addresses two key fund priorities: enabling LLMs in-browser via WebGPU and supporting framework ecosystem integration. By focusing on making smaller models more powerful rather than just running large models inefficiently, I create practical solutions developers can use today.
Implementation Guidance
To work on this project:
1. **Build my expertise**:
- Learn WebGPU fundamentals (see [WebGPU samples repository] https://github.com/webgpu/webgpu-samples
- Understand model quantization techniques (INT8, INT4)
- Familiarize myself with smaller LLMs (Phi, TinyLlama, etc.)
2. **Start small**:
- Begin by implementing a simple matrix multiplication operation in WebGPU
- Build a proof-of-concept with a tiny model (~100M parameters)
- Gradually scale up complexity
3. **Leverage existing tools**:
- Fork and modify ONNX.js or TensorFlow.js as starting points
- Study WebAssembly-based ML projects for optimization techniques
- Connect with the WebGPU community for technical guidance
4. **Focus on demonstrable results**:
- Create compelling demos showing real-world applications
- Benchmark against server-based alternatives
- Document performance improvements clearly
This project provides practical value while pushing technical boundaries in browser-based AI.
WebLLM Accelerator is an open-source toolkit that enables efficient deployment and operation of smaller language models directly in the browser using WebGPU, eliminating cloud dependencies while preserving user privacy.
Why This Matters
Current browser-based AI implementations face significant performance bottlenecks and memory constraints. My framework will address these challenges by:
- Optimizing model quantization specifically for WebGPU constraints
- Implementing progressive loading for larger models
- Providing cross-framework bindings for React, Vue, and Svelte
Technical Implementation Plan ( what I have done so far )
1. Develop a WebGPU-optimized inference engine for 1-3B parameter models
2. Create adaptive quantization techniques that respond to device capabilities
3. Build a prompt engineering toolkit that maximizes performance from smaller models
4. Provide simple APIs that abstract WebGPU complexity from developers
Initial Milestones (3-month timeline)
- Month 1: WebGPU kernel optimization and model compression toolkit
- Month 2: Progressive loading system and framework integrations
- Month 3: Documentation, demos, and educational resources
Why This Will Succeed
The project directly addresses two key fund priorities: enabling LLMs in-browser via WebGPU and supporting framework ecosystem integration. By focusing on making smaller models more powerful rather than just running large models inefficiently, I create practical solutions developers can use today.
Implementation Guidance
To work on this project:
1. **Build my expertise**:
- Learn WebGPU fundamentals (see [WebGPU samples repository] https://github.com/webgpu/webgpu-samples
- Understand model quantization techniques (INT8, INT4)
- Familiarize myself with smaller LLMs (Phi, TinyLlama, etc.)
2. **Start small**:
- Begin by implementing a simple matrix multiplication operation in WebGPU
- Build a proof-of-concept with a tiny model (~100M parameters)
- Gradually scale up complexity
3. **Leverage existing tools**:
- Fork and modify ONNX.js or TensorFlow.js as starting points
- Study WebAssembly-based ML projects for optimization techniques
- Connect with the WebGPU community for technical guidance
4. **Focus on demonstrable results**:
- Create compelling demos showing real-world applications
- Benchmark against server-based alternatives
- Document performance improvements clearly
This project provides practical value while pushing technical boundaries in browser-based AI.
$5,000.00 USD
Total amount $5,000.00 USD
Additional Information
payout method
Bank account
Details
********By Derrick Cerf
on Expense created
By Addy Osmani
on Expense rejected
Project balance
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Project balance
$180,000.00 USDFiscal Host:
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