Recalibrate batch-size estimate for the post-Phase-2 model size

The estimate_batch_size(training=True) calibration point was measured on
the pre-Phase-2 architecture (hidden_dim=512). Re-measured against the
current hidden_dim=1024 stack (Stage-2 secondary decoder + n_sec head
included): ~29696 batch size at ~7683 MiB VRAM.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

giant/config.py

@@ -53,14 +53,15 @@ def auto_device() -> torch.device:
     return torch.device("cpu")
 
 
-# Calibration point for estimate_batch_size(training=True): hidden_dim=512,
-# n_blocks=6, batch_size=131072 measured at ~8 GiB VRAM. Activation memory is
-# assumed to scale linearly with batch_size * hidden_dim * n_blocks (the
-# ResBlock stack dominates), so this is a rough estimate rather than a
-# guaranteed bound.
-_REF_BYTES = 8 * 1024**3
-_REF_BATCH_SIZE = 131072
-_REF_HIDDEN_DIM = 512
+# Calibration point for estimate_batch_size(training=True): hidden_dim=1024,
+# n_blocks=6, batch_size=29696 measured at ~7683 MiB VRAM (post-Phase-2
+# architecture, including the Stage-2 secondary decoder and n_sec head).
+# Activation memory is assumed to scale linearly with
+# batch_size * hidden_dim * n_blocks (the ResBlock stack dominates), so this
+# is a rough estimate rather than a guaranteed bound.
+_REF_BYTES = 7683 * 1024**2
+_REF_BATCH_SIZE = 29696
+_REF_HIDDEN_DIM = 1024
 _REF_N_BLOCKS = 6
 
 # Calibration point for estimate_batch_size(training=False): inference has no