diff --git a/docs/source/tutorials/hardwares/310p.md b/docs/source/tutorials/hardwares/310p.md
index 2cbbc40b..1e32995b 100644
--- a/docs/source/tutorials/hardwares/310p.md
+++ b/docs/source/tutorials/hardwares/310p.md
@@ -49,6 +49,25 @@ export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
 
 ### Online Inference on NPU
 
+```{warning}
+For Atlas 300I (310P), do not rely on `max-model-len` auto detection
+(omit `--max-model-len`), because it may cause OOM.
+
+Reason (current 310P attention path):
+- `AscendAttentionMetadataBuilder310` passes `model_config.max_model_len`
+  to `AttentionMaskBuilder310`.
+- `AttentionMaskBuilder310` builds a full causal mask with shape
+  `[max_model_len, max_model_len]` in float16, then casts it to FRACTAL_NZ.
+- In 310P `attention_v1` prefill/chunked-prefill
+  (`_npu_flash_attention` / `_npu_paged_attention_splitfuse`),
+  this explicit mask tensor is consumed directly, and there is no
+  compressed-mask path.
+
+So if auto resolves to a large context length, the mask allocation
+(`O(max_model_len^2)`) can exceed NPU memory and trigger OOM.
+Always set a conservative explicit value, for example `--max-model-len 4096`.
+```
+
 Run the following script to start the vLLM server on NPU (Qwen3-0.6B:1 card, Qwen2.5-7B-Instruct:2 cards, Pangu-Pro-MoE-72B: 8 cards):
 
 :::::{tab-set}
@@ -64,9 +83,9 @@ Run the following command to start the vLLM server:
    :substitutions:
 vllm serve Qwen/Qwen3-0.6B \
     --tensor-parallel-size 1 \
+    --max-model-len 4096 \
     --enforce-eager \
-    --dtype float16 \
-    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
+    --dtype float16
 ```
 
 Once your server is started, you can query the model with input prompts.
@@ -94,9 +113,9 @@ Run the following command to start the vLLM server:
    :substitutions:
 vllm serve Qwen/Qwen2.5-7B-Instruct \
     --tensor-parallel-size 2 \
+    --max-model-len 4096 \
     --enforce-eager \
-    --dtype float16 \
-    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
+    --dtype float16
 ```
 
 Once your server is started, you can query the model with input prompts.
@@ -124,9 +143,9 @@ Run the following command to start the vLLM server:
    :substitutions:
 vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
     --tensor-parallel-size 1 \
+    --max-model-len 4096 \
     --enforce-eager \
-    --dtype float16 \
-    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
+    --dtype float16
 ```
 
 Once your server is started, you can query the model with input prompts.
@@ -183,9 +202,9 @@ sampling_params = SamplingParams(max_completion_tokens=100, temperature=0.0)
 llm = LLM(
     model="Qwen/Qwen3-0.6B",
     tensor_parallel_size=1,
+    max_model_len=4096,
     enforce_eager=True, # For 300I series, only eager mode is supported.
     dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
-    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
 )
 # Generate texts from the prompts.
 outputs = llm.generate(prompts, sampling_params)
@@ -226,9 +245,9 @@ sampling_params = SamplingParams(max_completion_tokens=100, temperature=0.0)
 llm = LLM(
     model="Qwen/Qwen2.5-7B-Instruct",
     tensor_parallel_size=2,
+    max_model_len=4096,
     enforce_eager=True, # For 300I series, only eager mode is supported.
     dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
-    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
 )
 # Generate texts from the prompts.
 outputs = llm.generate(prompts, sampling_params)
@@ -269,9 +288,9 @@ sampling_params = SamplingParams(max_completion_tokens=100, top_p=0.95, top_k=50
 llm = LLM(
     model="Qwen/Qwen2.5-VL-3B-Instruct",
     tensor_parallel_size=1,
+    max_model_len=4096,
     enforce_eager=True, # For 300I series, only eager mode is supported.
     dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
-    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
 )
 # Generate texts from the prompts.
 outputs = llm.generate(prompts, sampling_params)