scriberr: correct the GPU 1 budget — nvidia-smi Free is 15,442 MiB, not total−used; 70 MiB spare beside intern-decision at 9.0 GiB

stacks/scriberr/README.md

@@ -136,8 +136,9 @@ tab. Keep the configured model on a free local seat.
 
 ## Parakeet memory and slicing (measured 2026-09-30)
 
-Scriberr shares fv-ml1 GPU 1 with intern-decision (~10.3 GB cap). The budget left
-for Scriberr is about 5.8 GB, so the compose file sets
+Scriberr shares fv-ml1 GPU 1 with intern-decision (9.0 GiB cap, 9,876 MiB card peak).
+GPU 1's nvidia-smi Free is 15,442 MiB, so the budget left for Scriberr is about 5.5 GB
+(70 MiB spare at both peaks), so the compose file sets
 `PARAKEET_CHUNK_THRESHOLD_SECS=120` and
 `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`. The numbers behind those
 settings are in the compose comments.

stacks/scriberr/compose.yaml

@@ -80,7 +80,9 @@ services:
       #   120 s + expandable_segments 5,496 · 60 s + expandable_segments 5,502
       # The floor, not the slice, dominates below ~120 s; expandable_segments is
       # what removes the fragmentation on top of it. 120 s + expandable fits
-      # beside intern-decision even at both peaks (295 MiB spare). Transcripts
+      # beside intern-decision (9.0 GiB cap, 9,876 MiB card peak) even at both
+      # peaks: 9,876 + 5,496 = 15,372 of GPU 1's 15,442 MiB nvidia-smi Free
+      # (70 MiB spare; Free is NOT total−used, the driver reserves ~640 MiB). Transcripts
       # change slightly: 95.8% word-sequence similarity vs 300 s (7,599 vs
       # 7,645 words); the diffs are mostly casing/punctuation spread through
       # the file, ~40 words at the 17 cuts. A-vs-A at 300 s: identical.