Petals

[secondtruth]

Unrelated project, in the same general area as DAIN:

https://github.com/bigscience-workshop/petals
Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading.

Differentiators

1. Focus on Advanced Distributed Training: While Petals primarily focuses on inference, DAIN is dedicated to distributed training and fine-tuning of AI models. We aim to support a wide range of advanced training methodologies, going beyond basic distributed training to push the boundaries of what's possible in collaborative AI development.

2. Connectivity-Aware Resource Allocation: DAIN places a strong emphasis on understanding and optimizing for network topology and connectivity characteristics. Our system is designed to make the most efficient use of heterogeneous volunteer resources, considering factors like compute power, memory, and network capabilities to optimize model distribution and training efficiency.

3. Volunteer-Centric Approach: Unlike Petals, which is moving towards payment systems, DAIN maintains a focus on volunteer computing. We believe in the power of community contributions and aim to create a system that incentivizes participation through means other than direct financial compensation.

4. Technical Innovation over Monetization: While Petals seems to be shifting focus towards payment systems, DAIN remains committed to pushing the boundaries of distributed AI training technology. Our primary goal is to advance the field of AI through technical innovation rather than developing monetization strategies.

Secondary Considerations

While our primary focus is on creating an efficient distributed training system, we acknowledge the importance of:

1. Bookkeeping: Tracking contributions, model versions, and training progress.

2. Hardware Support: While initially focusing on NVIDIA GPUs, we aim to expand support to other hardware in the future.

These aspects, while important, are considered secondary to our core mission of democratizing AI model training and optimizing distributed approaches of it.

— Information contains insights from @icec102