RalphLabs AI

The gap that matters in open AI development is not between the best model and the second best. It is between the best model and the one you can actually run. Ralph has spent its life on the wrong side of that gap. It was an open training recipe that improved itself, a network racing to make a shared pretraining recipe better, forever. That worked: the king changed autonomously. But the artifact was a recipe. You had to go train something before it was worth anything to you. The subnet is now aimed at the other side of the gap: compressing open models so they run where people actually are. PrismML made the case better than an argument could. Their Bonsai work takes a pinned model, leaves the architecture alone, and re-stores the weights at roughly 1.1 to 1.7 bits each. Much smaller, still useful, and people clearly wanted it. The method is essentially unpublished, and it is one lab doing this once. That is the shape of the opportunity, not a criticism of them. Compression is not a solved problem with a known best answer — it is a moving search across quantization schemes where the frontier shifts every few months and nobody owns it. One lab compresses a model and writes it up. A network can race it continuously, and pay whoever is currently winning. So the objective is now: one pinned open parent, architecture untouched, stored in fewer bits. Crowns are tiered by bit budget, and every crown publishes as downloadable open weights rather than a leaderboard position. The part that makes this a product instead of a scoreboard is that the architecture never changes. This is not a smaller model trained to imitate a bigger one. It is the model you already know, stored differently — smaller, not dumber. A submission whose shape does not match the parent is refused before any weights load. At 4-bit, the parent currently pinned goes from 1.26 GB to 315 MB. The objective changes; the machine does not. King-of-the-hill, the noise-floor statistics, the signed audit trail, and the anti-gaming work that has already caught and reverted two fraudulent crowns on the public record all carry over. github.com/RalphLabsAI/ralph… #LLM #quantization #Bittensor

The same model. Small enough to sit on your phone. Ralph’s 2.94 GB Qwen3-8B sub2 crown ran in PocketPal on 4 physical iPhones. All 4 loaded and answered locally via Metal: 10.58–18.54 tok/s. Video + raw receipts: huggingface.co/datasets/Ralp… #Compression #Bittensor #GGUF #QWEN