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 - **MLA attention** (q/kv-LoRA, interleaved partial RoPE) with **compressed KV-cache**: 576 floats/token instead of 32,768 (57× smaller — GLM-5.2 has 64 heads and no GQA).
 - **DeepSeek-V3-style sigmoid router** (noaux_tc, routed_scaling_factor), shared expert, first-3-dense layers.
 - **Native MTP speculative decoding** — GLM-5.2's own multi-token-prediction head (layer 78) drafts tokens that the main model verifies in one batched forward. **The head must be int8** (the converter does this by default): at int4 draft acceptance collapses to 0–4% and speculation never engages; at int8 it's 39–59% acceptance, **2.2–2.8 tokens/forward** (community-measured, [#8](https://github.com/JustVugg/colibri/issues/8)). Lossless *in exact arithmetic* — but **not byte-identical to non-speculative greedy in practice** ([#100](https://github.com/JustVugg/colibri/issues/100)). This isn't MTP-specific: colibrì's quantized integer kernels are shape-dependent, so any batched (S>1) or GPU forward rounds slightly differently from the single-token path, and int4 GLM-5.2 sits close enough to argmax ties that such a rounding change can flip a token. MTP, the CUDA expert tier, and batched prefill are three different ways to trip the same sensitivity (community-confirmed in #100: swapping only the kernel family forks greedy output on 3/5 prompts, with **zero speculation**). Every emitted token is still the argmax of a *valid* forward — the continuation stays correct — it just isn't the same stream. For byte-exact reproducibility: `DRAFT=0` (no speculation), plus `IDOT=0 COLI_CUDA=0` if you also want kernel-family/GPU independence. Under sampling, rejection sampling keeps the distribution correct. Honest caveat from the same measurement: on a **cold** cache each verified draft routes to extra experts (~660 → ~1100 expert-loads/token), so speculation can be a net *time* loss until the cache/pin warms up.
-- **Grammar-forced speculative drafts** (`GRAMMAR=file.gbnf`, [#48](https://github.com/JustVugg/colibri/issues/48)) — on constrained-output workloads (JSON/NDJSON, function calling, structured extraction) the grammar itself is a third draft source: wherever it admits exactly **one** legal byte (braces, quotes, key names, enum bodies), that forced span is tokenized and injected as pre-accepted drafts with ~1.0 acceptance — no draft head, no lookup table, and it engages even with the int4 MTP head from [#8](https://github.com/JustVugg/colibri/issues/8). It never constrains sampling: forced spans are verified in the same batch-union forward as any draft, so a wrong or out-of-sync grammar cannot change the output — worst case is rejected drafts, and an adaptive guard turns the source off below 50% acceptance. Byte-level GBNF subset (literals, char classes, `| ( ) ? * +`, comments); `GRAMMAR_DRAFT=n` caps the forced span per forward (default 24). Composes with `DRAFT`/MTP, which fill the free-text gaps between forced spans.
+- **Grammar-forced speculative drafts** (`GRAMMAR=file.gbnf`, [#48](https://github.com/JustVugg/colibri/issues/48)) — on constrained-output workloads (JSON/NDJSON, function calling, structured extraction) the grammar itself is a third draft source: wherever it admits exactly **one** legal byte (braces, quotes, key names, enum bodies), that forced span is tokenized and injected as pre-accepted drafts with ~1.0 acceptance — no draft head, no lookup table, and it engages even with the int4 MTP head from [#8](https://github.com/JustVugg/colibri/issues/8). It never constrains sampling: forced spans are verified in the same batch-union forward as any draft, so a wrong or out-of-sync grammar cannot change the output — worst case is rejected drafts, and an adaptive guard turns the source off below 50% acceptance. Byte-level GBNF subset (literals, char classes, `| ( ) ? * +`, comments); `GRAMMAR_DRAFT=n` caps the forced span per forward (default 24). Composes with `DRAFT`/MTP, which fill the free-text gaps between forced spans. Full reference — mechanism, measured A/Bs, when it pays, prior art: [docs/grammar-draft.md](docs/grammar-draft.md).
 - **True sampling** — temperature + nucleus, defaults tuned for int4 reality (0.7 / 0.90; the official 1.0 / 0.95 samples quantization noise from the tail).
 - **Integer-dot kernels** (Q8_0-style int8 activations, AVX2 `maddubs`): int8 matmuls 1.4–2.5× faster (119 GFLOP/s measured), int4 1.8× in batch — routing decided per shape by measurement (int4 single-row stays f32: it measured slower).
 - **MLA weight absorption** (DeepSeek trick) for decode: no per-token k/v reconstruction — the query absorbs `kv_b`, context is projected after attention. Validated exact: TF 32/32 and generation 20/20 with absorption forced everywhere.
diff --git a/docs/grammar-draft.md b/docs/grammar-draft.md
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+# Grammar-forced speculative drafts
+
+*The canonical reference for the `GRAMMAR=` draft source (method F). History: idea in
+[#48](https://github.com/JustVugg/colibri/issues/48), implementation in
+[#70](https://github.com/JustVugg/colibri/pull/70), consolidated write-up with A/B
+measurements and corrections in [#146](https://github.com/JustVugg/colibri/issues/146).*
+
+## The mechanism in one paragraph
+
+On constrained-output workloads (JSON/NDJSON, function calling, structured extraction)
+the grammar itself is a draft source. A byte-level GBNF subset is compiled into a
+set-of-stacks PDA; wherever the grammar admits **exactly one** legal next byte
+(braces, quotes, key names, enum bodies, fixed separators), the engine walks that
+forced span, tokenizes it, and injects it as pre-accepted draft tokens into the
+**same batch-union verify forward** used by MTP and n-gram drafts. It never
+constrains sampling: forced spans are *verified by the target model*, so a wrong,
+stale, or desynced grammar cannot change the output — worst case is rejected drafts,
+and an adaptive guard disables the source below 50% acceptance. It composes with
+`DRAFT`/MTP, which fill the free-text gaps between forced spans.
+
+## Why it pays disproportionately in this engine
+
+In a disk-streaming MoE, the marginal cost of a decode step is **unique expert bytes
+off disk**, not FLOPs. The batch-union forward loads each unique expert once for
+*all* positions in the verify batch, and structural tokens route heavily into experts
+the neighboring free-text positions already pulled in. So every accepted forced span
+converts almost directly into **disk reads avoided per emitted token**. On a dense
+CPU engine the same trick saves only compute; here it saves the scarcest resource.
+Fewer forwards also means less LRU churn, which compounds with cache hit rate.
+
+## Usage
+
+```bash
+GRAMMAR=fit.gbnf TEMP=0 PROMPT="..." ./glm 64 4 8
+```
+
+- `GRAMMAR=<file.gbnf>` — arm the draft source with a grammar. Byte-level GBNF
+  subset: literals (escapes `\" \\ \n \r \t \xHH`), byte classes `[...]`/`[^...]`,
+  rule refs, groups, postfix `? * +`, `|`, `#` comments. Root rule must be `root`.
+- `GRAMMAR_DRAFT=n` — cap the forced span per forward (default 24, max 48).
+- Arming is lazy (the walker starts at the first byte the root admits, skipping
+  preambles) and desync-tolerant (a non-conforming byte kills the walker for the
+  current span; it re-arms at the next opportunity).
+- The engine reports at end of run: `grammar: NN% acceptance (a/b forced drafts)`.
+
+## What to expect (measured, current `main`, M3 Max — details in #146)
+
+| workload shape | tok/forward | end-to-end |
+|---|---|---|
+| conforming compact NDJSON (PR #70 era) | 1.60 | large |
+| current-main NDJSON with sloppy spacing / long free-text fields | 1.21–1.22, 87% acceptance | ~+5% |
+| prose-dominated windows (preambles, long `reason` strings) | ~1.0 | ~nil |
+
+**The win tracks structural-span density.** Free-text content forces nothing;
+keys, separators, quotes and enum bodies force everything. Two practical levers:
+
+1. **Prompt for compact output** (short enum-like fields, no markdown fences).
+2. **Whitespace-tolerant grammars**: a compact-only grammar desyncs at the first
+   stray space and forfeits every span after it. Emitting an optional-whitespace
+   rule at separators costs those single bytes (two legal bytes → not forced) but
+   keeps the walker alive, so every multi-byte span after them still drafts.
+   Because drafts are verified, tolerance is strictly acceptance-positive.
+
+## Guarantees and limits
+
+- **Lossless by construction**: greedy output is byte-identical with and without
+  a grammar (verified in the #70 A/B); under sampling the rejection step preserves
+  the sampling distribution. The tokenization boundary of a forced byte span is
+  not guaranteed to coincide with the model's — verification absorbs the
+  difference (worst case the last draft of a span is rejected).
+- Adaptive shut-off below 50% acceptance, so a mismatched grammar degrades to
+  baseline speed, never below it for long.
+- Benchmarking note (learned the hard way, see #146): hold the PIN hot-store and
+  page-cache warmth constant across A/B rungs and report the expert hit-rate
+  column next to any tok/s claim.
+
+## Prior art
+
+The umbrella idea is not colibri's: **jump-forward decoding** (SGLang;
+[XGrammar](https://arxiv.org/abs/2411.15100)) skips grammar-deterministic tokens by
+*constraining output*, and Outlines' coalescence does the analogous FSM move. What
+this implementation adds: forced spans as a **draft source verified in the target's
+own forward** (lossless even under a wrong grammar, composes with MTP/n-gram in one
+union batch), deployed where the win is denominated in expert I/O rather than
+forward passes.