diff --git a/tests/kernels/moe/test_moe.py b/tests/kernels/moe/test_moe.py
index eddc395cc..eb3d9f8a8 100644
--- a/tests/kernels/moe/test_moe.py
+++ b/tests/kernels/moe/test_moe.py
@@ -396,6 +396,55 @@ def test_fused_moe(
         )
 
 
+def test_fused_moe_int64_overflow(monkeypatch, workspace_init):
+    """Regression test for int32 overflow in stride*offset products.
+
+    When chunking is disabled and M is large, stride_cm * offs_token can
+    exceed int32 max. Verifies the offs_token int64 cast (fix for #34413)
+    prevents overflow and produces correct results.
+
+    Reproduces the scenario from PR #34279.
+    """
+    # ~12 GB GPU memory needed for intermediate caches
+    free_mem = torch.cuda.mem_get_info()[0]
+    if free_mem < 12 * 1024**3:
+        pytest.skip("Insufficient GPU memory for overflow test")
+
+    set_random_seed(7)
+
+    m, n, k, e, topk = 100000, 2048, 1024, 8, 6
+    dtype = torch.bfloat16
+
+    # Disable chunking to expose the overflow-prone code path
+    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "10000000")
+
+    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
+    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
+    score = torch.randn((m, e), device="cuda", dtype=dtype)
+
+    # Verify the test exercises the overflow condition:
+    # C has shape (M, topk, N) where N = w1.size(1) = 2*n
+    # stride_cm = C.stride(1) = N, max offs_token = M * topk
+    # Product must exceed int32 max for this test to be meaningful
+    N = w1.size(1)
+    assert N * m * topk > 2**31 - 1, "Test params don't trigger int32 overflow"
+
+    fused_moe_fn = functools.partial(fused_moe, renormalize=False)
+
+    with set_current_vllm_config(vllm_config):
+        run_moe_test(
+            torch_moe,
+            fused_moe_fn,
+            a=a,
+            w1=w1,
+            w2=w2,
+            score=score,
+            topk=topk,
+            global_num_experts=e,
+        )
+
+
 @pytest.mark.parametrize("m,n,k", FUSED_MOE_MNK_FACTORS_SMALL_M)
 @pytest.mark.parametrize("e", NUM_EXPERTS_LARGE)
 @pytest.mark.parametrize("topk", TOP_KS_SMALL)
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index 5240f79be..a80978772 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -175,7 +175,8 @@ def fused_moe_kernel_gptq_awq(
     if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
         return
     offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(tl.int64)
-    offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
+    # Cast to int64 to prevent overflow in stride*offset products
+    offs_token = tl.load(sorted_token_ids_ptr + offs_token_id).to(tl.int64)
     token_mask = offs_token < num_valid_tokens
 
     off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
@@ -426,6 +427,9 @@ def fused_moe_kernel(
             pid_m,  # first element = pid_m
             num_valid_tokens,  # remaining elements = constant
         )
+    # Cast to int64 to prevent overflow in stride*offset products
+    # (e.g. stride_cm * offs_token can exceed int32 for large token counts)
+    offs_token = offs_token.to(tl.int64)
 
     token_mask = offs_token < num_valid_tokens