diff --git a/.buildkite/run-tpu-v1-test.sh b/.buildkite/run-tpu-v1-test.sh
index 2c356b8fe527..89252000f400 100755
--- a/.buildkite/run-tpu-v1-test.sh
+++ b/.buildkite/run-tpu-v1-test.sh
@@ -28,16 +28,14 @@ docker run --privileged --net host --shm-size=16G -it \
     && echo TEST_3 \
     && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
     && echo TEST_4 \
-    && python3 /workspace/vllm/examples/offline_inference/tpu.py \
+    && pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
     && echo TEST_5 \
-    && pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py \
+    && python3 /workspace/vllm/examples/offline_inference/tpu.py \
     && echo TEST_6 \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py \
+    && echo TEST_7 \
     && pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py" \
 
 
 # TODO: This test fails because it uses RANDOM_SEED sampling
 # && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
-
-# TODO: Re-enable this after fixing recompilation in quantization.
-# && echo TEST_4 \
-# && pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
diff --git a/vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py b/vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py
index 0bf090d7fab3..089314071d39 100644
--- a/vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py
+++ b/vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py
@@ -97,7 +97,8 @@ def apply_weights(self,
                                              block_size=-1,
                                              int4_weight=False,
                                              quantize_activation=True)
-
+        # `quantized_matmul` output is fp32, cast it down to bf16 for perf
+        out = out.to(x.dtype)
         # Explicitly capture control flow to make dynamo happy.
         # https://pytorch.org/docs/main/generated/exportdb/index.html#cond-branch-class-method # noqa: E501
         return cond(bias is None, self.no_add_bias, self.add_bias, [out, bias])
diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py
index 5401fff2bf19..4369d5a14af5 100644
--- a/vllm/v1/worker/tpu_model_runner.py
+++ b/vllm/v1/worker/tpu_model_runner.py
@@ -80,6 +80,7 @@ def __init__(
         self.enforce_eager = model_config.enforce_eager
         self.pin_memory = is_pin_memory_available()
         self.dtype = self.model_config.dtype
+        self._hidden_states_dtype = self.dtype
 
         self.is_multimodal_model = model_config.is_multimodal_model
         self.sliding_window = model_config.get_sliding_window()
@@ -758,10 +759,11 @@ def _dummy_run(self, kv_caches, num_tokens: int) -> None:
         torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
 
         with set_forward_context(attn_metadata, self.vllm_config, 0):
-            self.model(input_ids=input_ids,
-                       positions=position_ids,
-                       kv_caches=kv_caches,
-                       inputs_embeds=inputs_embeds)
+            out = self.model(input_ids=input_ids,
+                             positions=position_ids,
+                             kv_caches=kv_caches,
+                             inputs_embeds=inputs_embeds)
+        self._hidden_states_dtype = out.dtype
 
     def capture_model(self) -> None:
         """Compile the model."""
@@ -787,7 +789,7 @@ def capture_model(self) -> None:
             num_reqs_to_sample = MIN_NUM_SEQS
             dummy_hidden = torch.randn((num_tokens, hsize),
                                        device=device,
-                                       dtype=torch.bfloat16)
+                                       dtype=self._hidden_states_dtype)
             # Compile for [8, 16, .., 128,.., `self.max_num_reqs`]
             while True:
                 indices = torch.zeros(
@@ -810,7 +812,7 @@ def capture_model(self) -> None:
                     num_reqs_to_sample + 1, self.max_num_reqs)
         xm.wait_device_ops()
         end = time.perf_counter()
-        logger.info("Compilation finished in in %.2f [secs].", end - start)
+        logger.info("Compilation finished in %.2f [secs].", end - start)
         # Record the number cached XLA graph after warming up, this will be
         # used for checking there is no additional graph compilation during
         # runtime execution.
diff --git a/vllm/v1/worker/tpu_worker.py b/vllm/v1/worker/tpu_worker.py
index 4d9a113e39ee..7c81d1365628 100644
--- a/vllm/v1/worker/tpu_worker.py
+++ b/vllm/v1/worker/tpu_worker.py
@@ -105,8 +105,8 @@ def init_device(self):
 
         # Increase the cache size limit, which is the maximum number of
         # dynamo graphs that can be compiled.
-        # NOTE(woosuk): Usually, we compile 10-15 graphs for prefill and
-        # 30-40 graphs for decode. 128 is an arbitrary safe number.
+        # TODO (NickLucche) On gsm we compile 80+ graphs.
+        # Re-evaluate limit, with MM we may get close to this limit.
         torch._dynamo.config.cache_size_limit = 128
         # Use persistent cache to avoid XLA recompilation.
         # NOTE(woosuk): Set per-rank cache path since different ranks