FLAP trains 122B Qwen on GTX 1060

This is a strong attention-grabber, but it reads more like a capability demo than a full methodology write-up. Qwen3.5-122B-A10B is a sparse MoE model with 122B total parameters and 10B activated, so the “122B on 6GB” pitch is dramatic without being quite as impossible as a dense-model headline sounds.

• –FLAP’s positioning matches its homepage: fine-tune LLMs locally, privately, and without cloud bills.
• –If the demo is reproducible, the product could matter for hobbyists and small teams locked out of big-GPU training runs.
• –The missing details matter: batch size, precision, gradient checkpointing, optimizer-state handling, and whether this is true training or fragment-wise adaptation.
• –For developers, the real signal is a path toward model customization on commodity cards, not just a flashy benchmark stunt.