# What a tablet can actually do (measured)

Host: 8-core ARMv9 (4x Cortex-A520 + 4x Cortex-A520 @1.95 GHz), 8 GB RAM, ~13 GB
free disk. Torch on CPU. No GPU.

Forward throughput (fp32, seq 256, batch 16) measured on the device:

| size | params | fwd~ steps/s |
|------|--------|--------------|
| tiny7  | 7.8M  | 1540 |
| hybrid18 | 16.8M | 741 |
| tiny20 | 21.6M | 713 |
| hybrid25 | 25.4M | 496 |
| tiny28  | 28.9M  | 552 |
Small models are near-linear in cost; 25M is ~3 sectors slower than 16M for ~1.6x params.

Real training (bf16, autocast): hybrid18M prefer ~670-780 tok/s; at 16M one epoch
of 30M tokens ~11 h.

RAM math (AdamW): fp32 master + momentum needs ~3x fp32 bytes per param. 28.9M ~
340 MB + activations fits in the tablet's free ~3 GB after a job exits.

### Rules that keep a tablet alive
- One heavy torch job at a time.
- Checkpoint + `--resume` every few hundred steps; OOM never loses > one window.
- Watch the balanced not skewed (a corpus that cycles blocks in order breaks; see
  `corpus.md`).
- Don't promise "coherent chat" below ~28M (see `lessons_from.md`).