diff --git a/k8s/apps/llm-serving/reasoning.yaml b/k8s/apps/llm-serving/reasoning.yaml
index 34fe884..18ffdf1 100644
--- a/k8s/apps/llm-serving/reasoning.yaml
+++ b/k8s/apps/llm-serving/reasoning.yaml
@@ -45,21 +45,23 @@ spec:
       # to CPU DRAM instead of discarding them on preemption (max-num-seqs=4
       # + max-model-len=16384 means concurrent long sequences compete for
       # the same GPU KV space). No extra dependency, built into vLLM core.
-      # num_cpu_blocks=2000 hung the pod at startup ("Allocating 64 CPU
-      # tensors..." then nothing -- 64 is this model's layer count, one
-      # pinned host tensor per layer, each sized for every CPU block; 2000
-      # was oversized enough to stall pinning that much host memory, likely
-      # blowing well past the pod's memory limit). Dropped to a small,
-      # known-safe starting point -- confirm it actually comes up healthy,
-      # then watch real host memory usage and raise it deliberately rather
-      # than guessing a round number again. block_size=128 tokens matches
-      # vLLM's own example.
+      # num_cpu_blocks=2000 hung the pod at startup on the old model (2000 x
+      # ~32MB/block blew well past the pod's memory limit). num_cpu_blocks=32
+      # was the safe-recovery value after that -- only ~1GB of real DRAM
+      # (32 blocks x 128 tokens x 256KB/token-across-all-64-layers, fp16),
+      # basically a token-count safety valve, not real offload capacity.
+      # This model: 64 layers, 8 KV heads x 128 head_dim, fp16 -> ~256KB of
+      # KV per token across all layers -> ~32MB per 128-token block.
+      # num_cpu_blocks=256 -> ~8GB of actual DRAM offload (32,768 tokens),
+      # comfortably under the pod's 36Gi limit alongside the ~20GB bnb-4bit
+      # weights. Watch real host memory on boot before raising further --
+      # block_size=128 tokens matches vLLM's own example.
       # Note: 0.11.0 ships the original (fragmented, small-transfer-block)
       # version of this connector -- 0.12.0 consolidates KV data into one
       # contiguous block per request and is reported an order of magnitude
       # faster for this specific feature, so this is a real but not yet
       # optimal implementation until the image gets bumped.
-      - --kv-transfer-config={"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"num_cpu_blocks":32,"block_size":128}}
+      - --kv-transfer-config={"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"num_cpu_blocks":256,"block_size":128}}
       - --host=0.0.0.0
       - --port=8080
       env: