diff --git a/vllm/config.py b/vllm/config.py
index 9738d2fd0e00..1ae8673f7775 100644
--- a/vllm/config.py
+++ b/vllm/config.py
@@ -3140,6 +3140,14 @@ def _get_and_verify_max_len(
     # derived length from the HF model config.
     if max_model_len is None:
         max_model_len = int(derived_max_model_len)
+        if current_platform.is_tpu():
+            logger.warning(
+                "--max-model-len is not specified, "
+                "it's currently using model's default length %s, "
+                "which might be too large."
+                "Please input with --max-model-len based on your "
+                "request input length and output length, to avoid "
+                "unnecessary degradation.", max_model_len)
     elif max_model_len > derived_max_model_len:
         # Some models might have a separate key for specifying model_max_length
         # that will be bigger than derived_max_model_len. We compare user input
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
index 0ba14c4dee04..aefba620e189 100644
--- a/vllm/engine/arg_utils.py
+++ b/vllm/engine/arg_utils.py
@@ -1441,8 +1441,8 @@ class EngineArgs:
         # as the platform that vLLM is running on (e.g. the case of scaling
         # vLLM with Ray) and has no GPUs. In this case we use the default
         # values for non-H100/H200 GPUs.
+        from vllm.platforms import current_platform
         try:
-            from vllm.platforms import current_platform
             device_memory = current_platform.get_device_total_memory()
         except Exception:
             # This is only used to set default_max_num_batched_tokens
@@ -1463,11 +1463,37 @@ class EngineArgs:
             }
             default_max_num_seqs = 256
 
+        # tpu specific default values.
+        if current_platform.is_tpu():
+            default_max_num_batched_tokens_tpu = {
+                UsageContext.LLM_CLASS: {
+                    'V6E': 2048,
+                    'V5E': 1024,
+                    'V5P': 512,
+                },
+                UsageContext.OPENAI_API_SERVER: {
+                    'V6E': 1024,
+                    'V5E': 512,
+                    'V5P': 256,
+                }
+            }
+
         use_context_value = usage_context.value if usage_context else None
         if (self.max_num_batched_tokens is None
                 and usage_context in default_max_num_batched_tokens):
-            self.max_num_batched_tokens = default_max_num_batched_tokens[
-                usage_context]
+            if current_platform.is_tpu():
+                chip_name = current_platform.get_device_name()
+                if chip_name in default_max_num_batched_tokens_tpu[
+                        usage_context]:
+                    self.max_num_batched_tokens = \
+                        default_max_num_batched_tokens_tpu[
+                            usage_context][chip_name]
+                else:
+                    self.max_num_batched_tokens = \
+                        default_max_num_batched_tokens[usage_context]
+            else:
+                self.max_num_batched_tokens = default_max_num_batched_tokens[
+                    usage_context]
             logger.debug(
                 "Setting max_num_batched_tokens to %d for %s usage context.",
                 self.max_num_batched_tokens, use_context_value)
diff --git a/vllm/platforms/tpu.py b/vllm/platforms/tpu.py
index d5923557a211..9c95e6d3fa08 100644
--- a/vllm/platforms/tpu.py
+++ b/vllm/platforms/tpu.py
@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING, Optional, Union
 
 import torch
+from tpu_info import device
 
 import vllm.envs as envs
 from vllm.inputs import ProcessorInputs, PromptType
@@ -54,7 +55,8 @@ class TpuPlatform(Platform):
 
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
-        return "tpu"
+        chip_type, _ = device.get_local_chips()
+        return f"TPU {chip_type.name}"
 
     @classmethod
     def get_device_total_memory(cls, device_id: int = 0) -> int: