# Decision record (WP33 TITAN-MoE)

Format: D-NNN · date · decision · rationale · alternatives considered · consequences.

## D-001 · 2026-11-01 · Project location and numbering
Decision: the project is UQO Working Paper No. 33, stored at `Recherche-UQO/ia/titan-moe/` (category `ia/`, after WP30/31),
future bare repo `gitsrv:srv/git/wp33_uqo.git`. Inside, the directory structure follows the mandate (`titan/`, `metal/`,
`transport/`, `adapters/`, `tests/`, `benchmarks/`, `research/`, `figures/`, `paper/`, `artifacts/`, `scripts/`).
Rationale: one numbered folder per paper is the series standard (`HARMONISATION.md`); the mandate's `wp33_titan_moe/` name is
honoured as the repository name. Deviation noted in README "Notes".

## D-002 · 2026-11-01 · Reference MLX backend first; third-party code as shallow git-ignored clones
Decision: Gate B is a pure-MLX reference implementation on top of `mlx.core` ops (`gather_qmm`, `gather_mm`), not a fork of
mlx-lm. Colibri, mlx, mlx-lm and llama.cpp are shallow-cloned under `third_party/` for audit only; hashes are recorded.
Rationale: mandate ("reference MLX backend first; optimized C++/Metal/Swift only where justified"); keeps the repo small.

## D-003 · 2026-11-01 · First model: OLMoE-1B-7B-0125-Instruct, 4-bit MLX conversion
Decision: `mlx-community/OLMoE-1B-7B-0125-Instruct-4bit` (rev `e2384419`, Apache-2.0, 16 layers, 64 experts, top-8, no shared expert).
Rationale: small (3.9 GB), fully open licence, supported by mlx-lm (`olmoe.py`), llama.cpp (GGUF) and Colibri (OLMoE engine),
so three baselines can be compared on the same architecture; 64 experts × 16 layers gives a non-trivial routing/caching problem.
Alternatives: Qwen1.5-MoE-A2.7B (non-Apache licence), gpt-oss-20b (MXFP4, larger). Medium/large candidates (Qwen3-30B-A3B,
gpt-oss-120b, Qwen3-235B) are deferred to Gate C/F and need an explicit download authorization above 20 GB.

## D-004 · 2026-11-01 · Unreachable nodes are excluded, not assumed
Decision: nodes that did not answer SSH within 15 s at inventory time are excluded from WP33 experiments until re-probed;
the paper's hardware table only lists machines whose specifications were read live (`sysctl`, `system_profiler`).

## D-005 · 2026-11-01 · Slot-table execution with `gather_qmm` on stacked slot buffers
Decision: each layer owns a `SlotTable` of `capacity` resident experts in stacked MLX buffers; expert ids are remapped to
slot ids and executed with the same `mx.gather_qmm` kernel mlx-lm uses, so the compute path is identical for resident and
just-loaded experts. Loading = in-place slice assignment (`buf[slot] = tensor`), measured at 0.305 ms for the 9 tensors of
one OLMoE expert into 216 MB buffers (`benchmarks/raw/gateB/design/slot_update_microbench_laptop.txt`), i.e. MLX reuses the
buffer rather than copying it. Prefill rows whose unique-expert union exceeds the capacity are chunked greedily by rows (exact).
Alternatives measured: per-expert `quantized_matmul` loop 0.43 ms/layer vs 0.26 ms for gather_qmm (1 token, top-8);
re-stacking 8 experts per call 0.50 ms/layer. Consequence: the host must read the router's choice (`tolist()`) before each
layer's expert kernels — the unavoidable synchronization of demand loading, shared with Colibri; its cost is measured in Gate C.

## D-006 · 2026-11-01 · Activation parity
Decision: use mlx-lm's compiled `swiglu` (silu(gate)·up in one fused kernel). Rationale: an unfused fp16 `up·sigmoid(gate)·gate`
gave max |Δlogit| = 0.0195 vs mlx-lm; the fused version gives 0.0 (bit-identical) — see `benchmarks/raw/tests/numerical_*.json`.

## D-007 · 2026-11-01 · Lazy slot installs, batched demand misses
Decision: `SlotTable.ensure()` reads all missing experts of a layer, installs them as lazy slice assignments and never calls
`mx.eval` itself; the layer's `gather_qmm` consumes the updated buffers in the same graph. Rationale: the first version evaluated
the whole table after every miss (one GPU sync per missed expert): at 16 slots decode went from 28/41 tok/s to 51/73 tok/s
(general/code, held-out) once removed (`benchmarks/raw/gateC/prefetch_online_laptop/smoke*.jsonl`). Per-miss cost on the laptop
with a page-cached file is now 0.21 ms (`benchmarks/raw/gateC/design/miss_cost_breakdown_laptop.txt`: pread 0.13 + numpy/MLX
copy 0.06 + install) against 0.43 ms from a cold SSD and ≈ 28 ms over 1 GbE.

## D-008 · 2026-11-01 · Medium model download (17.2 GB) under the standing ≤ 20 GB rule
Decision: download `mlx-community/Qwen3-30B-A3B-Instruct-2507-4bit` (rev `e9675aa3`, Apache-2.0, 48 layers × 128 experts, top-8)
on M3U96a (cluster coordinator for Gates E/F), then rsync to worker nodes over the LAN rather than re-downloading. Rationale:
OLMoE (3.6 GB of experts) fits in every node's RAM, so the memory-budget regimes of H1/H3 need a model whose experts exceed
the budget of the smaller nodes (36–64 GB) and whose expert count (6 144) gives a realistic routing problem. gpt-oss-120b (62 GB)
is deferred until explicitly authorized.

## D-009 · 2026-11-01 · Expert-contiguous storage layout for the SSD tier
Decision: the SSD tier reads from a TITAN blob (`experts.titan`, one 4 KiB-aligned contiguous record per expert, index JSON)
generated once from the checkpoint by `scripts/convert_layout.py`; the source checkpoint is never modified and remains the
source of dense weights. Rationale: measured 9 scattered reads per expert in the HF safetensors (expert span 1.5 GB) → cold
16-slot decode 8–10 tok/s; with parallel reads 18; with the contiguous layout 36 tok/s (4.5× over v1). Colibri performs an
equivalent conversion to its own container; we keep safetensors for everything else so mlx-lm remains the exact reference.