diff --git a/docs/pfi/training-throughput-playbook.md b/docs/pfi/training-throughput-playbook.md
index 6079df3..1cc720b 100644
--- a/docs/pfi/training-throughput-playbook.md
+++ b/docs/pfi/training-throughput-playbook.md
@@ -325,10 +325,12 @@ The point is that it should be a *decision* made before the window, not a
 discovery made after — because the alternative it forecloses may be an
 architecture choice, and by then you have already trained.
 
-## 3.11 Base-viability pre-flight — three greps, before you pick
+## 3.11 Base-viability pre-flight — four checks, before you pick
 
 Run this on any candidate base BEFORE committing a training window. Each check
-is minutes; skipping them cost a night in 2026-08.
+is minutes; skipping them cost a night in 2026-08. Check 4 was added 2026-09-09
+after a measurement showed the *newest* carrier in a sweep training 2.6x slower
+than a dense one 2.3x its size.
 
 **1. Does it fit for TRAINING?** BF16 weights on one card, with room for the
 real peak — not the weight figure.
@@ -369,6 +371,66 @@ from `LlamaForCausalLM`. `mistral_large_3.py` greps as 0 for both and inherits
 only MRO resolution is right. (Same failure as asserting a substring instead of
 an effective value.)
 
+**4. Is it a HYBRID linear-attention model — and is the fused kernel installed?**
+⚠ **Newest is not fastest, and the penalty is an order of magnitude, not a
+percentage.** Modern "small" checkpoints increasingly interleave Mamba-style
+`linear_attention` blocks with a minority of real attention layers. Without a
+fused kernel (`mamba_ssm`, `causal_conv1d`, or `fla`) `transformers` runs a
+reference implementation, and the whole premise of picking a small carrier dies.
+
+```python
+import importlib
+from transformers import AutoConfig
+
+path = "/home/infra-ops/carriers/Qwen3.5-0.8B-Base"   # or a hub repo id
+cfg = AutoConfig.from_pretrained(path)
+tc = getattr(cfg, "text_config", None) or cfg          # multimodal configs nest it
+lt = list(getattr(tc, "layer_types", []) or [])
+print("full_attention:", lt.count("full_attention"),
+      "linear_attention:", lt.count("linear_attention"))
+
+for m in ("mamba_ssm", "causal_conv1d", "fla", "kernels"):
+    try:
+        importlib.import_module(m); print(f"{m:14s} OK")
+    except Exception:
+        print(f"{m:14s} MISSING")
+```
+
+Both halves are needed: a hybrid shape with the kernel present is fine, and a
+dense shape does not care either way. It is the **intersection** that is slow.
+The reusable instrument is `scripts/training-probes/bench_lora_step.py`, whose
+raw output for this comparison is committed beside it.
+
+**Measured, pfi-gx10 (GB10), 2026-09-09** — identical harness, n=10 per arm,
+seq 4096, LoRA r=32 on `q,k,v,o`+MLP, bf16 `sdpa`, grad-ckpt on, spreads
+0.6–2.6%, no fused kernel present:
+
+| carrier | shape | params | tok/s |
+|---|---|---|---|
+| `Qwen3-1.7B-Base` | dense | 1.755 B | **1,415** |
+| `Qwen3-0.6B-Base` | dense | 0.616 B | **2,399** |
+| `Qwen3.5-0.8B-Base` | 18 linear / 6 full | 0.765 B | **540** |
+
+The dense 1.755 B carrier trains **2.6x faster than the hybrid 0.765 B one** on
+2.3x the parameters — ~6x per parameter — while adapting *more* LoRA modules
+(196 vs 96, since dense has attention in every layer). Grad checkpointing is not
+the cause (19%, and it saves 2.6x memory: keep it on). Batching is not the lever:
+both families are at the box's roofline at batch 1 (dense 1,415→1,439 and hybrid
+540→546 tok/s at batch 4), so the gap is the kernel path, not a batching artefact.
+
+⚠ **Two more things a hybrid checkpoint brings that a dense one does not.**
+(a) Its `*-Base` release may be **multimodal**: `Qwen3.5-*-Base` ships a vision
+tower (153 `model.visual.*` Linear tensors at 0.8B, 297 at 2B) plus an MTP head,
+all of which `target_modules="all-linear"` attaches LoRA to and then trains on
+pure text — the same defect the Gemma-4 harness audit caught. Loading through
+`AutoModelForCausalLM` drops both for free, **but it renames modules**
+(`model.layers.N.*` vs the serving class's `model.language_model.layers.N.*`), so
+adapter binding needs §3.10's serving-path proof and a sampled-target-changed
+check. (b) **Cross-document packing is unsafe**: SSM recurrent state runs along
+the sequence and an attention mask does not reset it, so packed documents bleed
+in every linear-attention layer. One document per sequence, or prove the
+boundary signal is honoured.
+
 **Worked results, 2026-08-25:**
 
 | base | fits (1) | MoE mapping (2) | LoRA (3) | verdict |