diff --git a/pyproject.toml b/pyproject.toml
index 97651afee..a5d41c673 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -167,6 +167,7 @@ depthwise_seperable_CNN = "depthwise_seperable_CNN"
 [tool.typos.default.extend-words]
 iy = "iy"
 tendencias = "tendencias"
+indx = "indx"
 # intel cpu features
 tme = "tme"
 dout = "dout"
diff --git a/tests/lora/test_gptoss_tp.py b/tests/lora/test_gptoss_tp.py
index 750ccd7c9..14d0ff47d 100644
--- a/tests/lora/test_gptoss_tp.py
+++ b/tests/lora/test_gptoss_tp.py
@@ -69,41 +69,54 @@ def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
         assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])
 
 
-def test_gpt_oss_lora(gptoss20b_lora_files):
-    llm = vllm.LLM(
-        MODEL_PATH,
-        max_model_len=1024,
-        enable_lora=True,
-        max_loras=4,
-        max_lora_rank=8,
-        max_num_seqs=2,
-        max_num_batched_tokens=2048,
-        compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
-            cudagraph_specialize_lora=False,
-        ),
-    )
+@pytest.mark.parametrize("mxfp4_use_marlin", [True, False])
+def test_gpt_oss_lora(
+    monkeypatch: pytest.MonkeyPatch, gptoss20b_lora_files, mxfp4_use_marlin
+):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_MXFP4_USE_MARLIN", "1" if mxfp4_use_marlin else "0")
+        llm = vllm.LLM(
+            MODEL_PATH,
+            max_model_len=1024,
+            enable_lora=True,
+            max_loras=4,
+            max_lora_rank=8,
+            max_num_seqs=2,
+            max_num_batched_tokens=2048,
+            compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
+                cudagraph_specialize_lora=False,
+            ),
+        )
 
-    generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
-    generate_and_test(llm, gptoss20b_lora_files, lora_id=2)
+        generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
+        generate_and_test(llm, gptoss20b_lora_files, lora_id=2)
 
 
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize("fully_sharded_loras", [False, True])
-def test_gpt_oss_lora_tp2(gptoss20b_lora_files, fully_sharded_loras):
-    llm = vllm.LLM(
-        MODEL_PATH,
-        max_model_len=1024,
-        enable_lora=True,
-        max_loras=2,
-        max_num_seqs=2,
-        max_num_batched_tokens=2048,
-        tensor_parallel_size=2,
-        gpu_memory_utilization=0.8,
-        fully_sharded_loras=fully_sharded_loras,
-        compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
-            cudagraph_specialize_lora=False,
-        ),
-    )
+@pytest.mark.parametrize("mxfp4_use_marlin", [True, False])
+def test_gpt_oss_lora_tp2(
+    monkeypatch: pytest.MonkeyPatch,
+    gptoss20b_lora_files,
+    fully_sharded_loras,
+    mxfp4_use_marlin,
+):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_MXFP4_USE_MARLIN", "1" if mxfp4_use_marlin else "0")
+        llm = vllm.LLM(
+            MODEL_PATH,
+            max_model_len=1024,
+            enable_lora=True,
+            max_loras=2,
+            max_num_seqs=2,
+            max_num_batched_tokens=2048,
+            tensor_parallel_size=2,
+            gpu_memory_utilization=0.8,
+            fully_sharded_loras=fully_sharded_loras,
+            compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
+                cudagraph_specialize_lora=False,
+            ),
+        )
 
-    generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
-    generate_and_test(llm, gptoss20b_lora_files, lora_id=2)
+        generate_and_test(llm, gptoss20b_lora_files, lora_id=1)
+        generate_and_test(llm, gptoss20b_lora_files, lora_id=2)
diff --git a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
index 0b006e156..dff8a9f3a 100644
--- a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
+++ b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
@@ -502,16 +502,18 @@ class UnfusedOAITritonExperts(BaseOAITritonExperts):
         )
 
         self.activation(
-            activation, intermediate_cache2, intermediate_cache1.view(-1, N)
+            activation,
+            intermediate_cache2,
+            intermediate_cache1.view(-1, N)[gather_indx.dst_indx],
         )
 
         # matmul_ogs grouped reduction fuse sum across multiple experts:
-        # y[dst_ind // n_expts_act, :] += x[src_ind, :]
+        # y[dst_indx // n_expts_act, :] += x
         # Need to set n_expts_act to 1 to unfuse moe_sum
         routing_data.n_expts_act = 1
 
         matmul_ogs(
-            intermediate_cache2,
+            intermediate_cache2[gather_indx.src_indx],
             w2,
             self.quant_config.w2_bias,
             routing_data,