Q Labs: Computational Depth Is the Missing Scaling Axis for LLMs

Q Labs Research argues that computational depth is an overlooked scaling axis: while parameters, data and test-time inference have grown by orders of magnitude, network depth has stagnated around 100 layers since GPT-3, with 128-layer models still showing continued improvement.

2026-09-23 ~ 2026-09-24 · 4 related posts

• Researchers argue LLMs are severely depth-bottlenecked: the missing scaling axis — madhavsinghal_ · 2026-09-23
• Researchers argue computational depth is the missing scaling axis, stuck at ~100 layers since GPT-3 — burny_tech · 2026-09-24
• Q Labs: LLMs are depth-bottlenecked, loss keeps improving to 128 layers — rickasaurus · 2026-09-24

1 near-duplicate retellings: burny_tech