diff --git a/dlrm_data_pytorch.py b/dlrm_data_pytorch.py
index b815205c..4f14cf9d 100644
--- a/dlrm_data_pytorch.py
+++ b/dlrm_data_pytorch.py
@@ -730,6 +730,28 @@ def make_random_data_and_loader(args, ln_emb, m_den,
         rand_seed=args.numpy_rand_seed
     )  # WARNING: generates a batch of lookups at once
 
+    test_data = RandomDataset(
+        m_den,
+        ln_emb,
+        args.data_size,
+        args.num_batches,
+        args.mini_batch_size,
+        args.num_indices_per_lookup,
+        args.num_indices_per_lookup_fixed,
+        1,  # num_targets
+        args.round_targets,
+        args.data_generation,
+        args.data_trace_file,
+        args.data_trace_enable_padding,
+        reset_seed_on_access=True,
+        rand_data_dist=args.rand_data_dist,
+        rand_data_min=args.rand_data_min,
+        rand_data_max=args.rand_data_max,
+        rand_data_mu=args.rand_data_mu,
+        rand_data_sigma=args.rand_data_sigma,
+        rand_seed=args.numpy_rand_seed
+    )
+
     collate_wrapper_random = collate_wrapper_random_offset
     if offset_to_length_converter:
         collate_wrapper_random = collate_wrapper_random_length
@@ -743,7 +765,17 @@ def make_random_data_and_loader(args, ln_emb, m_den,
         pin_memory=False,
         drop_last=False,  # True
     )
-    return train_data, train_loader
+
+    test_loader = torch.utils.data.DataLoader(
+        test_data,
+        batch_size=1,
+        shuffle=False,
+        num_workers=args.num_workers,
+        collate_fn=collate_wrapper_random,
+        pin_memory=False,
+        drop_last=False,  # True
+    )
+    return train_data, train_loader, test_data, test_loader
 
 
 def generate_random_data(
diff --git a/dlrm_s_pytorch.py b/dlrm_s_pytorch.py
index 9b2f4522..eb352664 100644
--- a/dlrm_s_pytorch.py
+++ b/dlrm_s_pytorch.py
@@ -1103,8 +1103,9 @@ def run():
         # input and target at random
         ln_emb = np.fromstring(args.arch_embedding_size, dtype=int, sep="-")
         m_den = ln_bot[0]
-        train_data, train_ld = dp.make_random_data_and_loader(args, ln_emb, m_den)
+        train_data, train_ld, test_data, test_ld = dp.make_random_data_and_loader(args, ln_emb, m_den)
         nbatches = args.num_batches if args.num_batches > 0 else len(train_ld)
+        nbatches_test = len(test_ld)
 
     args.ln_emb = ln_emb.tolist()
     if args.mlperf_logging:
@@ -1447,9 +1448,6 @@ def run():
         if args.quantize_emb_with_bit != 32:
             dlrm.quantize_embedding(args.quantize_emb_with_bit)
             # print(dlrm)
-        assert (
-            args.data_generation == "dataset"
-        ), "currently only dataset loader provides testset"
 
     print("time/loss/accuracy (if enabled):")
 
@@ -1599,7 +1597,7 @@ def run():
                     )
                     should_test = (
                         (args.test_freq > 0)
-                        and (args.data_generation == "dataset")
+                        and (args.data_generation in ["dataset", "random"])
                         and (((j + 1) % args.test_freq == 0) or (j + 1 == nbatches))
                     )