diff --git a/tests/unit_tests/worker/test_gather_mm_embeddings.py b/tests/unit_tests/worker/test_gather_mm_embeddings.py
new file mode 100644
index 0000000000..ccfbf4350d
--- /dev/null
+++ b/tests/unit_tests/worker/test_gather_mm_embeddings.py
@@ -0,0 +1,230 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Unit tests for _gather_mm_embeddings position computation,
+specifically the 2D padded batching fix.
+"""
+
+from dataclasses import dataclass
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+
+from vllm.multimodal.inputs import PlaceholderRange
+
+
+@dataclass
+class _FakeRequestState:
+    num_computed_tokens: int
+    mm_features: list
+
+
+@dataclass
+class _FakeMMFeature:
+    identifier: str
+    mm_position: PlaceholderRange
+
+
+def _make_runner_stub(requests, encoder_cache, max_tokens=1024):
+    """Build a minimal mock that satisfies _gather_mm_embeddings."""
+    runner = MagicMock()
+    runner.requests = requests
+    runner.encoder_cache = encoder_cache
+    runner.uses_mrope = False
+
+    # is_mm_embed buffer: expose a plain CPU tensor via .cpu property
+    cpu_buf = torch.zeros(max_tokens, dtype=torch.bool)
+    buf = MagicMock()
+    buf.cpu = cpu_buf
+    buf.copy_to_gpu = lambda n: cpu_buf[:n].clone()
+    runner.is_mm_embed = buf
+    return runner
+
+
+def _make_scheduler_output(num_scheduled_tokens, total=None):
+    so = SimpleNamespace()
+    so.num_scheduled_tokens = num_scheduled_tokens
+    so.total_num_scheduled_tokens = total or sum(
+        num_scheduled_tokens.values()
+    )
+    return so
+
+
+def _call_gather(runner, scheduler_output, req_ids, **kwargs):
+    """Call _gather_mm_embeddings as an unbound method on the stub."""
+    from vllm_gaudi.v1.worker.hpu_model_runner import HPUModelRunner
+    return HPUModelRunner._gather_mm_embeddings(
+        runner, scheduler_output, req_ids, **kwargs
+    )
+
+
+# ------------------------------------------------------------------ #
+# Test: single request, no padding (baseline)
+# ------------------------------------------------------------------ #
+def test_single_request_no_padding():
+    """One request, 10 scheduled tokens, MM embed at offset 2 length 4."""
+    hidden = 8
+    enc_out = torch.randn(4, hidden)
+    req_id = "req-0"
+
+    requests = {
+        req_id: _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature(
+                    identifier="hash-a",
+                    mm_position=PlaceholderRange(offset=2, length=4),
+                ),
+            ],
+        ),
+    }
+    encoder_cache = {"hash-a": enc_out}
+    sched = _make_scheduler_output({req_id: 10}, total=10)
+    runner = _make_runner_stub(requests, encoder_cache)
+
+    mm_embeds, is_mm = _call_gather(
+        runner, sched, [req_id], total_num_scheduled_tokens=10
+    )
+
+    assert len(mm_embeds) == 1
+    assert mm_embeds[0].shape == (4, hidden)
+    # Positions 2..5 should be marked True
+    expected = torch.zeros(10, dtype=torch.bool)
+    expected[2:6] = True
+    assert torch.equal(is_mm, expected)
+
+
+# ------------------------------------------------------------------ #
+# Test: two requests, contiguous 1D layout (no padded_seq_len)
+# ------------------------------------------------------------------ #
+def test_two_requests_contiguous():
+    """Two requests laid out contiguously: [req0: 8 tokens][req1: 8 tokens].
+    req0 has MM at offset 1 length 3, req1 has MM at offset 0 length 2.
+    """
+    hidden = 4
+    enc_a = torch.randn(3, hidden)
+    enc_b = torch.randn(2, hidden)
+
+    requests = {
+        "r0": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("ha", PlaceholderRange(offset=1, length=3)),
+            ],
+        ),
+        "r1": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("hb", PlaceholderRange(offset=0, length=2)),
+            ],
+        ),
+    }
+    encoder_cache = {"ha": enc_a, "hb": enc_b}
+    sched = _make_scheduler_output({"r0": 8, "r1": 8}, total=16)
+    runner = _make_runner_stub(requests, encoder_cache)
+
+    mm_embeds, is_mm = _call_gather(
+        runner, sched, ["r0", "r1"], total_num_scheduled_tokens=16
+    )
+
+    assert len(mm_embeds) == 2
+    expected = torch.zeros(16, dtype=torch.bool)
+    # r0: positions 1..3
+    expected[1:4] = True
+    # r1: starts at idx 8 (contiguous), positions 0..1 → 8..9
+    expected[8:10] = True
+    assert torch.equal(is_mm, expected)
+
+
+# ------------------------------------------------------------------ #
+# Test: two requests, 2D padded layout (padded_seq_len given)
+# ------------------------------------------------------------------ #
+def test_two_requests_padded_2d():
+    """Two requests in a [2, 16] padded tensor.
+    req0 has 10 real tokens, MM at offset 2 length 4.
+    req1 has 8 real tokens, MM at offset 0 length 3.
+    padded_seq_len = 16.
+    Flattened total = 2 * 16 = 32.
+    """
+    hidden = 4
+    enc_a = torch.randn(4, hidden)
+    enc_b = torch.randn(3, hidden)
+
+    requests = {
+        "r0": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("ha", PlaceholderRange(offset=2, length=4)),
+            ],
+        ),
+        "r1": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("hb", PlaceholderRange(offset=0, length=3)),
+            ],
+        ),
+    }
+    encoder_cache = {"ha": enc_a, "hb": enc_b}
+    total = 2 * 16  # padded total
+    sched = _make_scheduler_output({"r0": 10, "r1": 8}, total=total)
+    runner = _make_runner_stub(requests, encoder_cache)
+
+    mm_embeds, is_mm = _call_gather(
+        runner, sched, ["r0", "r1"],
+        total_num_scheduled_tokens=total,
+        padded_seq_len=16,
+    )
+
+    assert len(mm_embeds) == 2
+    expected = torch.zeros(total, dtype=torch.bool)
+    # r0 (batch_row=0): positions 0*16 + 2 .. 0*16 + 5 → 2..5
+    expected[2:6] = True
+    # r1 (batch_row=1): positions 1*16 + 0 .. 1*16 + 2 → 16..18
+    expected[16:19] = True
+    assert torch.equal(is_mm, expected)
+
+
+# ------------------------------------------------------------------ #
+# Test: padded vs contiguous positions differ
+# ------------------------------------------------------------------ #
+def test_padded_positions_differ_from_contiguous():
+    """Verify that with padding, the second request's MM positions
+    start at batch_row * padded_seq_len, NOT at sum-of-scheduled-tokens."""
+    hidden = 4
+    enc_a = torch.randn(2, hidden)
+    enc_b = torch.randn(2, hidden)
+
+    requests = {
+        "r0": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("ha", PlaceholderRange(offset=0, length=2)),
+            ],
+        ),
+        "r1": _FakeRequestState(
+            num_computed_tokens=0,
+            mm_features=[
+                _FakeMMFeature("hb", PlaceholderRange(offset=0, length=2)),
+            ],
+        ),
+    }
+    encoder_cache = {"ha": enc_a, "hb": enc_b}
+
+    padded_seq_len = 32
+    total = 2 * padded_seq_len
+    sched = _make_scheduler_output({"r0": 10, "r1": 10}, total=total)
+    runner = _make_runner_stub(requests, encoder_cache)
+
+    _, is_mm_padded = _call_gather(
+        runner, sched, ["r0", "r1"],
+        total_num_scheduled_tokens=total,
+        padded_seq_len=padded_seq_len,
+    )
+
+    # Second request's MM positions should be at 32..33, not 10..11
+    assert is_mm_padded[32].item() is True
+    assert is_mm_padded[33].item() is True
+    # Contiguous offset 10 should NOT be set
+    assert is_mm_padded[10].item() is False
diff --git a/vllm_gaudi/v1/worker/hpu_model_runner.py b/vllm_gaudi/v1/worker/hpu_model_runner.py
index de205d6a2e..0ba8c6c0d4 100644
--- a/vllm_gaudi/v1/worker/hpu_model_runner.py
+++ b/vllm_gaudi/v1/worker/hpu_model_runner.py
@@ -1613,15 +1613,16 @@ def _get_model_mm_inputs(
             # total tokens across the padded layout and pass padded_seq_len
             # so _gather_mm_embeddings can map positions correctly.
             padded_seq_len = None
+            effective_total_tokens = total_num_scheduled_tokens
             if token_ids.ndim == 2 and token_ids.shape[0] > 1:
                 padded_seq_len = token_ids.shape[-1]
-                total_num_scheduled_tokens = (
+                effective_total_tokens = (
                     token_ids.shape[0] * token_ids.shape[1]
                 )
 
             mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output,
                                                                 req_ids,
-                                                                total_num_scheduled_tokens=total_num_scheduled_tokens,
+                                                                total_num_scheduled_tokens=effective_total_tokens,
                                                                 padded_seq_len=padded_seq_len)
             # TODO: Only get embeddings for valid token_ids. Ignore token_ids[<pad_idxs>] # noqa
             # This may require moving multimodal input preps into _prepare_inputs,        # noqa