Raw gate records for Soup's layer-streaming feature, published as written.
These are not a report assembled after the fact. They are the working records kept while each item was built and verified, so they contain the failures, the assumptions that turned out wrong, and the numbers that were measured and then discarded — in the order those things happened.
They are the evidence behind the preprint:
Makazhan, A. (2026). Exact Layer Streaming: LoRA Fine-Tuning of an 8B Model on a 4 GB Laptop GPU. Zenodo. 10.5281/zenodo.21771064
| File | What it gates | Headline |
|---|---|---|
gate-v0.72.0-layer-streaming.md |
The streaming path itself | Bit-exactness vs a resident reference; 3B bf16 trained on a 4 GB card |
gate-v0.72.2-nf4.md |
NF4 quantised streaming | Llama-3.1-8B at 119.6 tok/s in a 3.32 GB peak |
gate-v0.72.3-breadth.md |
Nine architectures, batching, accumulation, resume, disk tier | Peak-VRAM predictor at 0.85% worst-case error; accumulation is per-token I/O-neutral |
gate-v0.72.4-preference-losses.md |
DPO / ORPO / SimPO / KTO over the streaming engine | DPO's reference model costs no extra weights — 0.914x the SFT peak, against +730.44 MB for a real second instance |
Every number was measured on one machine:
- GPU — RTX 3050 Laptop, 4 GB (4.29 GB usable)
- Host — 16.9 GB RAM, NVMe
- OS — Windows 11
Windows/WDDM matters for reading these: it spills into shared host memory rather
than raising CUDA out of memory, so a run completing is not evidence that its
configuration fits. That is why peak VRAM is reported alongside every throughput
figure, and why the fit decision refuses rather than warns.
- Throughput is quoted with the SM clock it was taken at. This card's boost clock varies about 13% between sessions, so a fraction-of-ceiling stated without its clock is not meaningful. Where a GEMM ceiling is compared against, it was measured in the same session.
- The correctness reference always matches the numerics under test — a streamed NF4 run is compared against a resident NF4 run, never against resident bf16, which would hide a real defect inside quantisation error.
- Derived figures are labelled as arithmetic. Where a line says "1M tokens = 2.3 h", that is division, not a measured wall-clock run.
The implementation ships in Soup under Apache-2.0. Reproduction commands are in Appendix A of the paper; the correctness protocol runs as part of the project's test suite, so a regression in bit-exactness fails CI rather than reaching a user.
pip install 'soup-cli[train]'