[Docs] Cite where the 28.8 GB lookup table figure comes from

README.md

@@ -29,7 +29,8 @@ engine's performance is not ours. It comes from four decisions:
 - **Long prompts are read in big pieces** (up to 8,192 tokens), which is why a long document is read at over
   1,000 tokens a second, and a follow-up reuses the context already read instead of starting over.
 - **A 28.8 GB lookup table lives on the SSD** and is read a few rows per token, so it costs disk bandwidth
-  instead of memory.
+  instead of memory. It is the model's second file: one `per_layer_token_embd.weight` tensor of 160 × 320,001,536
+  values at 4 bits — 28,800,138,240 bytes, decimal GB, 26.8 GiB.
 - **Guess, then check.** The model's built-in draft layer guesses up to three tokens and the model verifies all
   of them in one pass (2.4–3.2 tokens per pass), so an answer arrives 1.6–1.8× sooner with the same text.
 

docs/GLOSSARY.md

@@ -57,6 +57,12 @@ guesses; the big model still decides every word, so answers do not change. See a
 The large lookup table (28.8 GB for Qwen3.8-Flash-Next) that Strata reads a few rows of per token from the SSD
 instead of holding in memory. `--ple-gguf` points at it; `--ple-io` chooses how it is read.
 
+The size is not a round number chosen by a doc: it is the model's **second file**, one tensor
+`per_layer_token_embd.weight` of 160 × 320,001,536 values stored as IQ4_NL (18 bytes per 32 values), which is
+160 × 320,001,536 × 18/32 = **28,800,138,240 bytes**. "28.8 GB" throughout these guides is decimal GB — the same
+file reads 26.8 GiB in `df`. A pack that stores the table in another type is bigger: Unsloth's UD-Q6_K_XL keeps it
+as Q8_0 (170 bytes per 160-value row) and the same table becomes 54.4 GB.
+
 ## The engine
 
 **Engine**