v3 tested the documented mitigation (raise global_batch to amortize the
per-step all-reduce). Isolated back-to-back A/B on 4× RTX 5090, dim384/12L,
seq256:
global_batch 32 (8/rank) → 3163 tok/s
global_batch 256 (64/rank)→ 3200 tok/s (8× batch, +1.2%, within noise)
8× larger batch = 1/8 the all-reduces per token, yet no speedup → all-reduce
is NOT the bottleneck. GPU util 0–15%, mem ~2–3 GB/32 GB → the workload is
launch-bound: the single-sequence model design (each sequence its own tiny
forward/backward, per-op kernel launches) starves the GPU, and batching only
adds proportionally more serial launches. Real fix is batched (multi-sequence)
forward so GEMMs fill the GPU — a T4/T5 autograd/model change, not a batch knob.
Bucketed/overlapped all-reduce stays deferred (no value until launch-bound is
fixed). KI-1 kept Open with the corrected root cause.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Surfaced by v2 (world=4, global_batch=32): ~3593 tok/s, no speedup vs v1
single-GPU. Root cause + proposed fixes recorded; also consolidates deferred
T7 items (bf16, activation recompute) and the large-vocab modeling note.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
