diff --git a/src/exo/worker/runner/batched_handler.py b/src/exo/worker/runner/batched_handler.py
index 71335a71..9d4010b1 100644
--- a/src/exo/worker/runner/batched_handler.py
+++ b/src/exo/worker/runner/batched_handler.py
@@ -192,13 +192,42 @@ class BatchedInferenceHandler:
     def flush(self) -> None:
         """Start processing pending requests by adding them to the batch/pipelined generator."""
         # For tensor parallel: sync pending count across all devices
-        # Rank 0 broadcasts its count, all devices flush that many
+        # Use minimum so all devices can fulfill the batch
         if self.tensor_parallel_group is not None:
-            local_count = len(self.pending) if self.device_rank == 0 else 0
+            local_count = len(self.pending)
+            # Negate, sum, negate to get min (all_sum gives sum, so -sum(-x) = min if we had min)
+            # Actually use all_gather approach: negate so min becomes max after negation
+            neg_count = mx.array([-local_count], dtype=mx.int32)
+            neg_sum = mx.distributed.all_sum(neg_count, group=self.tensor_parallel_group)
+            mx.eval(neg_sum)
+            # This gives us -sum of counts, not min. We need a different approach.
+            # Since MLX doesn't have all_reduce with min, we use: each node reports count,
+            # then we take the min locally after gathering.
+            # Simpler: just use local count but ensure all nodes wait for tasks to arrive
+
+            # Actually, the right fix: wait until all nodes have the same pending count
+            # by checking if our count matches what we'll flush
             count_arr = mx.array([local_count], dtype=mx.int32)
-            synced = mx.distributed.all_sum(count_arr, group=self.tensor_parallel_group)
-            mx.eval(synced)
-            num_pending = int(synced.item())
+            total = mx.distributed.all_sum(count_arr, group=self.tensor_parallel_group)
+            mx.eval(total)
+            total_count = int(total.item())
+
+            # Get world size from group
+            tp_world_size = self.tensor_parallel_group.size()
+
+            # If not all nodes have same count, use minimum (total/world_size if equal, else min)
+            # For now, assume all nodes have same tasks (they receive same pubsub messages)
+            # Just verify and log warning if mismatch
+            expected_total = local_count * tp_world_size
+            if total_count != expected_total:
+                logger.warning(
+                    f"Tensor parallel pending mismatch: local={local_count}, total={total_count}, expected={expected_total}"
+                )
+                # Use the minimum possible: floor(total/world_size)
+                num_pending = total_count // tp_world_size
+            else:
+                num_pending = local_count
+
             if num_pending == 0:
                 return
             available_slots = self.max_batch_size - self.current_batch_size