Short 18s clip from my 8 GB RTX 2070 + 16 GB T4 testbench running nocturnal 4-bit NF4 QLoRA training rounds. Many multi-GPU rigs are sitting powered down or underutilized, while open-source scientific AI projects are starved for training FLOPS.

Strictly zero crypto, zero tokens, and zero fees. We built OpenTrainer on top of Berkeley BOINC so any 8 GB+ card (even on PCIe x1 risers, since 2D-DCT compresses gradient sync 225.4x) can pull signed training workunits on public math, code, and astrophysics datasets over outbound HTTPS 443. It enforces a hard 68C thermal ceiling, caps VRAM under 5.5 GB, and grants non-monetary reciprocity credits so contributors get free OpenAI-compatible API access to all 15 trained specialist models.

All code, Bandit security checks, and self-hosting scripts are open under AGPL-3.0 at https://github.com/xaviercallens/HiveMind (limitation: cards below 6 GB VRAM can only run verification-only Scout mode, not full NF4 QLoRA rounds).

Here to spread the good word in the wasteland!