Refresh card with final measurements

README.md

@@ -45,6 +45,30 @@ list is libc, libm and libgomp.
 | This package (ternary-3) | 248.74 |
 | **Result** | **+1.10% perplexity** — marginal (95% CI [1.0003x, 1.0219x], t = +2.01) |
 
+
+### Against the 4-bit formats the hardware runs natively
+
+NVIDIA Blackwell executes NVFP4 and MXFP4 in the tensor cores with no software
+decode. Cascadia derives a per-element band instead of storing a per-block
+scale, which costs real decode time -- so the question is whether it buys
+anything. Every format below was scored through one evaluation path on the
+same 119,574 tokens, with the same window-clustered pairing.
+
+| format | bytes/weight | perplexity | vs this package |
+|---|---:|---:|---|
+| bf16 (uncompressed) | 2.0000 | 242.65 | — |
+| **Cascadia ternary-3** | 0.6746 | 244.80 | — |
+| NVFP4 (e2m1 + e4m3 / 16) | 0.5625 | 266.74 | **1.090x worse** (t = +7.80) |
+| int4 + fp16 scale / 32 | 0.5625 | 270.84 | **1.106x worse** (t = +9.55) |
+| MXFP4 (e2m1 + ue8m0 / 32) | 0.5312 | 314.84 | **1.286x worse** (t = +22.44) |
+
+Cascadia is 20% larger than NVFP4 and resolvably better than all three, while its own gap to bf16 is *not* statistically resolvable on this corpus (t = +1.52). The result is size-dependent and does not generalise: on the smaller LFM2.5-230M, int4 with a per-32 fp16 scale beats Cascadia while being smaller.
+
+Reproduce with `tools/format_shootout.py`. Its `cascadia` arm reconstructs
+this package and is checked against the C runtime's own perplexity before any
+number is reported; a mismatch aborts rather than prints.
+
+
 117,530 paired tokens of FineWeb-Edu in **229 independent
 512-token windows**. Both models score identical tokens and are
 compared per token, which cuts the standard error 10.3x versus