2026-10-11 17:09 UTC

memory-efficient-training

band: coolmomentum: stable score: 0.002
temperature history

Episodes (4)

Independent benchmarks will determine whether the fused chunked KL-loss implementation enables mathematically equivalent 32K-context knowledge distillation in under 6GB of VRAM with linear rather than quadratic memory scaling.
expiredknownscott: medium
Independent use will determine whether Imprint can fine-tune MoE language models larger than system RAM at practical speed and without material quality loss.
expiredconvergesscott: medium
Independent replication will determine whether DiffusionBlocks can materially reduce training memory for deep vision, diffusion, and language models without unacceptable quality or compute-efficiency losses.
expirednovelscott: medium
Ullis’s creator claims its RWKV-8 Heron and 1-bit ROSA design can train a 300M-parameter, 32-layer model in about 1.5GB of RAM on a base M1 Mac, making substantial local model training feasible on commodity Apple Silicon.
expiredconvergesscott: medium

Trajectory notes