diff --git a/python/test/test_alltoallv_mscclpp.py b/python/test/test_alltoallv_mscclpp.py
index cdd4397c..2e55b98d 100644
--- a/python/test/test_alltoallv_mscclpp.py
+++ b/python/test/test_alltoallv_mscclpp.py
@@ -154,54 +154,77 @@ def main():
         print(f"  Local copy verified: {local_ok}")
         print(f"  {'PASS' if local_ok else 'FAIL'}")
     
-    # Test 3: Performance benchmark across message sizes (1KB to 128MB)
+    # Test 3: Performance benchmark with variable sizes (1KB to 128MB avg per peer)
     if rank == 0:
-        print("\n[Test 3] Performance benchmark (1KB to 128MB per rank)")
-        print(f"  {'Msg Size':>10s}  {'Iters':>5s}  {'Total (ms)':>10s}  {'Lat (us)':>10s}  {'algBW(GB/s)':>12s}")
+        print("\n[Test 3] Variable-size performance benchmark (1KB to 128MB avg per peer)")
+        print(f"  {'Avg Size':>10s}  {'Iters':>5s}  {'Total (ms)':>10s}  {'Lat (us)':>10s}  {'algBW(GB/s)':>12s}")
         print(f"  {'-'*10}  {'-'*5}  {'-'*10}  {'-'*10}  {'-'*12}")
 
-    # Message sizes: 1KB, 4KB, 16KB, 64KB, 256KB, 1MB, 4MB, 16MB, 64MB, 128MB
+    # Message sizes: average bytes sent to each peer
     msg_sizes = [1 << s for s in range(10, 28) if s % 2 == 0]  # powers of 4 from 1KB to 64MB
     msg_sizes.append(128 * 1024 * 1024)  # add 128MB
 
-    for msg_size in msg_sizes:
-        input_size = msg_size * world_size
-        n_elems = input_size // 4  # float32 = 4 bytes
+    for avg_msg_size in msg_sizes:
+        # Build a variable send matrix: send_matrix[i][j] = bytes rank i sends to rank j.
+        # Use a deterministic seed so all ranks compute the same matrix.
+        # Sizes vary from 0.5× to 1.5× of avg_msg_size (in float32 elements).
+        import random
+        random.seed(12345)
+        avg_elems = avg_msg_size // 4  # float32 = 4 bytes
+        send_matrix = []
+        for i in range(world_size):
+            row = []
+            for j in range(world_size):
+                # Random factor between 0.5 and 1.5
+                factor = 0.5 + random.random()
+                elems = max(1, int(avg_elems * factor))
+                row.append(elems)
+            send_matrix.append(row)
 
-        input_tensor = torch.randn(n_elems, dtype=torch.float32, device='cuda')
-        output_tensor = torch.empty_like(input_tensor)
+        input_split_sizes = send_matrix[rank]
+        output_split_sizes = [send_matrix[j][rank] for j in range(world_size)]
+
+        total_send = sum(input_split_sizes)
+        total_recv = sum(output_split_sizes)
+
+        input_tensor = torch.randn(total_send, dtype=torch.float32, device='cuda')
+        output_tensor = torch.empty(total_recv, dtype=torch.float32, device='cuda')
 
         # Fewer warmup/iters for very large sizes
-        n_warmup = 3 if msg_size >= 16 * 1024 * 1024 else 5
-        n_iters = 5 if msg_size >= 64 * 1024 * 1024 else (10 if msg_size >= 4 * 1024 * 1024 else 20)
+        n_warmup = 3 if avg_msg_size >= 16 * 1024 * 1024 else 5
+        n_iters = 5 if avg_msg_size >= 64 * 1024 * 1024 else (10 if avg_msg_size >= 4 * 1024 * 1024 else 20)
 
         # Warmup
         for _ in range(n_warmup):
-            alltoallv.all_to_all_single(input_tensor, output=output_tensor)
+            alltoallv.all_to_all_single(
+                input_tensor, output=output_tensor,
+                input_split_sizes=input_split_sizes,
+                output_split_sizes=output_split_sizes)
         torch.cuda.synchronize()
 
         # Benchmark
         start = time.perf_counter()
         for _ in range(n_iters):
-            alltoallv.all_to_all_single(input_tensor, output=output_tensor)
+            alltoallv.all_to_all_single(
+                input_tensor, output=output_tensor,
+                input_split_sizes=input_split_sizes,
+                output_split_sizes=output_split_sizes)
         torch.cuda.synchronize()
         elapsed = time.perf_counter() - start
 
-        # Algorithm bandwidth: total data received per rank / time
-        # Each rank receives msg_size bytes from each of the other (world_size-1) peers
-        # plus msg_size from itself (local copy). Total recv = input_size = msg_size * world_size.
-        # Report unidirectional algBw (same convention as nccl-test).
-        total_bytes = input_size * n_iters
+        # Algorithm bandwidth: total bytes received per rank / time (unidirectional)
+        total_recv_bytes = total_recv * 4  # float32
+        total_bytes = total_recv_bytes * n_iters
         bandwidth_gbps = total_bytes / elapsed / 1e9
         latency_us = elapsed / n_iters * 1e6
 
         if rank == 0:
-            if msg_size >= 1024 * 1024:
-                size_str = f"{msg_size // (1024*1024)}MB"
-            elif msg_size >= 1024:
-                size_str = f"{msg_size // 1024}KB"
+            if avg_msg_size >= 1024 * 1024:
+                size_str = f"{avg_msg_size // (1024*1024)}MB"
+            elif avg_msg_size >= 1024:
+                size_str = f"{avg_msg_size // 1024}KB"
             else:
-                size_str = f"{msg_size}B"
+                size_str = f"{avg_msg_size}B"
             print(f"  {size_str:>10s}  {n_iters:>5d}  {elapsed*1000:>10.2f}  {latency_us:>10.1f}  {bandwidth_gbps:>12.2f}")
     
     # Cleanup