Training a frontier model requires billions in capital for H100 clusters. Can a distributed community effort for ever compete with that? I’m interested in whether projects like Petals or various blockchain-based compute networks are actually viable for training something that can realistically rival the depth of a GPT-5 class model.
3 answers
Distributed training is theoretically possible, but the latency between consumer internet connections is a massive bottleneck compared to the NVLink interconnects in a dedicated data center. However, we are seeing success with "swarm intelligence" approaches where smaller models are trained independently and then merged or used in a MoE setup. This isn't exactly training a 10T parameter model from scratch, but it allows the open-source community to build highly specialized "experts" that, when combined, can rival the versatility of a closed-source giant.
Do you believe that specialized datasets are more important than raw compute when trying to bridge the performance gap between these models?
The biggest hurdle isn't the training; it's the data curation and the RLHF process that requires thousands of human annotators.
True, Melissa, but RLAIF (AI Feedback) is becoming a viable alternative that the open-source community is using to automate the alignment process quite effectively.
You hit the nail on the head, Gregory. Modern research shows that "textbook-quality" data can allow a 7B model to outperform a 70B model trained on raw web crawls. The open-source community is currently leading the way in curated, high-token-quality datasets, which is a much more efficient way to close the gap than just throwing more GPUs at the problem.