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-# Draft 2 — reply to Ben (costs + news), 2026-07-11
+# Draft 3 — reply to Ben (costs + news), 2026-07-11
 
-Subject: Re: costs — numbers you can put in front of accounting, and some news since the draft
+Subject: Re: costs — numbers for accounting, and some news since the draft
 
 Hi Ben,
 
 Great to hear — and yes, AsymEP it is ;)
 
-News first, because it changes what the credits would buy. Since the draft, the scaling line moved to a cascade form: L *distinct* standard transformer blocks, inference is a plain forward pass, EP only in training.
+News first, because it changes what the credits buy. Since the draft:
 
-- **A standard 12-block transformer (42.7M) trained through a full epoch with zero backpropagation anywhere in the loop — and it tells coherent stories.** A one-click notebook that samples from it is ready whenever you'd like to play with it.
-- As far as we can verify, **it is already the largest fully backprop-free language model trained to date**: the 1.07B optical-DFA result trains 400M of its parameters and keeps BP at the readout; the ES line's from-scratch model is a ~2M integer GRU.
-- **At matched tuning, EP is indistinguishable from its tuned backprop twin** (4k steps); at full-epoch the gap is 0.050 nats — and every centinat of it has a named mechanism and a measured dial (estimator SNR vs. nudge amplitude: the same physics an analog chip will meet, rehearsed in fp32).
-- **Muon's advantage over AdamW transfers to EP gradients at full magnitude** (−0.13 CE at this scale) — backprop-free training runs on a modern optimizer stack.
+- **A standard 12-layer transformer trained through a full epoch with zero backpropagation anywhere in the training loop — and it tells coherent stories.** Inference is a plain forward pass; a one-click notebook that samples from it is ready whenever you'd like to play.
+- As far as we can verify, **it is already the largest neural network fully trained without backpropagation** — the 1.07B optical-DFA result trains 400M of its parameters and keeps BP at the readout; the ES line's billion-scale results are fine-tuning of BP-pretrained models, its from-scratch model a ~2M integer GRU.
+- **Every previous no-backprop attempt, at any scale, concedes a quality gap to backprop. We measure none.** At matched tuning EP is statistically indistinguishable from its tuned backprop twin; at full-epoch the residual is 0.050 nats — and every centinat of it has a named mechanism and a measured dial (estimator SNR vs. nudge amplitude: the same physics an analog chip will meet, rehearsed in fp32). Muon's advantage over AdamW transfers to EP gradients at full magnitude — this runs on a modern optimizer stack.
 
-On costs, anchored on measured throughput (TF32 validated bit-exact for this trainer; EP = 3.2× BP FLOPs, measured, not estimated):
+On costs, anchored on measured throughput (TF32 validated bit-exact; EP = 3.2× BP FLOPs, measured):
 
 | run | cost, 1.4× failure buffer included |
 |---|---|
 | 300M × 6B tok — recipe validation on a real corpus | ~$1k |
-| **1B × 20B tok (Chinchilla-optimal)** | **~$12k — inside the cap** |
-| 3B × 25B tok | ~$25–30k (two capped, checkpointed segments) |
+| **1B × 20B tok, Chinchilla-optimal** | **~$12k — inside the cap** |
 
-The buffer is small because the failure model is not a guess: the three instabilities we met this quarter are each mechanistically diagnosed and closed (a β-schedule, a block-architecture change, an amplitude floor), and runs checkpoint every 5k steps — **a failed run costs a segment, not the run.** One day on your instances (~$500) converts these numbers from ±30% to ±10–15%; happy to run that calibration first.
+The buffer is small because the failure model is not a guess: the three instabilities we met this quarter are each diagnosed and mechanistically closed, and runs checkpoint every 5k steps — **a failed run costs a segment, not the run.** One day on your instances (~$500) converts ±30% into ±10–15%; happy to run that calibration first.
 
-Two questions so we can plan honestly: is the ~$20k a per-run cap inside a larger envelope, or the envelope itself? And does the backprop-twin control (≈1/3 the EP cost, needed for the head-to-head claim) draw from the same pool?
+The honest question on our minds is the next octave: **is 3B at full Chinchilla thinkable, and is 7–8B thinkable at all?** (~$25–30k and ~$50k at today's measured throughput — beyond a single capped run.) We are not asking for that now. The proposal is to deliver everything above within the cap first, and let those results decide whether the bigger conversation is worth having.
 
-No rush on the draft — it has since been restructured around a control-map spine, so I'd send you the updated PDF before you invest reading time.
+Separately — and this may interest Rain directly — **we are planning a small analog-hardware LM demo, and the recipe was deliberately designed so that every trained operation has an analog implementation**: divisive (current-mode) normalizations, fixed I/Q rotations for position, translinear gates, the classic subthreshold-exp/KCL circuit for attention's softmax, and learning itself as EP's two local phases. A component-by-component hardware map exists; happy to share it.
+
+Two small accounting questions: is the ~$20k a per-run cap inside a larger envelope, or the envelope itself? And does the backprop-twin control (≈1/3 the EP cost, needed for the head-to-head claim) draw from the same pool?
+
+No rush on the draft — it has since been restructured; I'd send the updated PDF before you invest reading time.
 
 Best,
 Yuren