diff --git a/vllm_omni/model_executor/layers/quantization/cpu_int4.py b/vllm_omni/model_executor/layers/quantization/cpu_int4.py
index 39caa2cf9..d943d398b 100644
--- a/vllm_omni/model_executor/layers/quantization/cpu_int4.py
+++ b/vllm_omni/model_executor/layers/quantization/cpu_int4.py
@@ -155,9 +155,23 @@ def quantize_groupwise(w: torch.Tensor, group: int):
     return pack_nibbles(q.reshape(n, k)), scale, zero
 
 
-REPACK_ROWS = 512
+REPACK_ROWS = 2048
 """Output channels repacked at a time, to bound the load-time peak.
 
+2048 because it is both the fastest and nearly the leanest. Repacking the
+131072-row tied embedding, 24 threads:
+
+    rows        chunks    time      int32 transient
+    whole            1   208 ms         1024 MiB
+    512            256   116 ms            4 MiB
+    2048            64    79 ms           16 MiB
+    8192            16    89 ms           64 MiB
+    32768            4   129 ms          256 MiB
+
+Chunking is *faster* than packing the whole matrix -- the working set stays in
+cache -- so this costs nothing at load; the earlier suspicion that it cost
+30 s of init came from a pass the harness had flagged as contended.
+
 ``unpack_nibbles`` expands 4-bit codes to int32 -- 4 bytes per weight, eight
 times the packed form -- and the kernel's packer wants int32. Doing that for a
 whole matrix makes the transient the largest thing in the process: 104 MiB for