diff --git a/examples/asr/conf/asr_streaming_inference/cache_aware_rnnt.yaml b/examples/asr/conf/asr_streaming_inference/cache_aware_rnnt.yaml
index 84730998b21f..2d098db40c01 100644
--- a/examples/asr/conf/asr_streaming_inference/cache_aware_rnnt.yaml
+++ b/examples/asr/conf/asr_streaming_inference/cache_aware_rnnt.yaml
@@ -13,7 +13,7 @@ asr:
     fused_batch_size: -1
     greedy:
       use_cuda_graph_decoder: false  # Disabled due to issues with decoding
-      enable_per_stream_biasing: false  # Per-stream biasing in decoder
+      enable_per_stream_biasing: true  # Per-stream biasing in decoder
       max_symbols: 10
       # n-gram LM
       ngram_lm_model: null  # The path to built '.nemo' NGPU-LM model
diff --git a/nemo/collections/asr/inference/pipelines/cache_aware_rnnt_pipeline.py b/nemo/collections/asr/inference/pipelines/cache_aware_rnnt_pipeline.py
index 1367f0514247..2b4de83dac1c 100644
--- a/nemo/collections/asr/inference/pipelines/cache_aware_rnnt_pipeline.py
+++ b/nemo/collections/asr/inference/pipelines/cache_aware_rnnt_pipeline.py
@@ -40,6 +40,7 @@
     get_confidence_utils,
 )
 from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
+from nemo.utils import logging
 
 if TYPE_CHECKING:
     from nemo.collections.asr.inference.itn.inverse_normalizer import AlignmentPreservingInverseNormalizer
@@ -306,6 +307,36 @@ def cache_aware_transcribe_step(
             eos_flags.append(request.is_last)
 
         previous_hypotheses = [state.get_previous_hypothesis() for state in states]
+
+        try:
+            decoding_computer = self.asr_model.asr_model.decoding.decoding.decoding_computer
+            biasing_enabled = decoding_computer.per_stream_biasing_enabled
+        except AttributeError:
+            decoding_computer = None
+            biasing_enabled = False
+
+        if not biasing_enabled and any(state.has_biasing_request() for state in states):
+            logging.warning("Biasing request is not empty, but decoder does not support per-stream biasing. Skipping")
+
+        # Handle per-stream biasing: add biasing models to multi_model if needed
+        if biasing_enabled:
+            for i, (request, state, previous_hyp) in enumerate(zip(requests, states, previous_hypotheses)):
+                if state.has_biasing_request():
+                    if state.options.biasing_cfg.multi_model_id is None:
+                        if state.options.biasing_cfg.auto_manage_multi_model:
+                            state.options.biasing_cfg.add_to_multi_model(
+                                tokenizer=self.asr_model.tokenizer,
+                                biasing_multi_model=decoding_computer.biasing_multi_model,
+                            )
+                        else:
+                            logging.warning(
+                                "Biasing request is not empty, not auto managed and not compiled. Skipping"
+                            )
+                    if previous_hyp is None:
+                        previous_hypotheses[i] = Hypothesis.empty_with_biasing_cfg(state.options.biasing_cfg)
+                    else:
+                        previous_hyp.biasing_cfg = state.options.biasing_cfg
+
         context, mapping = self.context_manager.get_context(stream_ids)
 
         prompt_vectors = None
@@ -344,6 +375,16 @@ def cache_aware_transcribe_step(
                 state.cleanup_after_eou()
                 ready_state_ids.add(request.stream_id)
 
+        # Cleanup per-stream biasing models when stream ends
+        if biasing_enabled:
+            for request, state in zip(requests, states):
+                # only the first request contains biasing options; biasing options for the stream are stored in state
+                if request.is_last and state.has_biasing_request():
+                    if state.options.biasing_cfg.auto_manage_multi_model:
+                        state.options.biasing_cfg.remove_from_multi_model(
+                            biasing_multi_model=decoding_computer.biasing_multi_model
+                        )
+
     def transcribe_step_for_feature_buffers(self, fbuffers: list[FeatureBuffer]) -> None:
         """
         Transcribes the feature buffers in a streaming manner.
diff --git a/nemo/collections/asr/inference/streaming/state/cache_aware_rnnt_state.py b/nemo/collections/asr/inference/streaming/state/cache_aware_rnnt_state.py
index d3efd80e4396..c9374c37ba26 100644
--- a/nemo/collections/asr/inference/streaming/state/cache_aware_rnnt_state.py
+++ b/nemo/collections/asr/inference/streaming/state/cache_aware_rnnt_state.py
@@ -51,7 +51,7 @@ def set_previous_hypothesis(self, previous_hypothesis: Hypothesis) -> None:
         """
         self.previous_hypothesis = previous_hypothesis
 
-    def get_previous_hypothesis(self) -> Hypothesis:
+    def get_previous_hypothesis(self) -> Hypothesis | None:
         """
         Get the previous hypothesis
         Returns:
diff --git a/tests/functional_tests/L2_Speech_Transcription_Speech_to_Text_Inference_Boost_GT.sh b/tests/functional_tests/L2_Speech_Transcription_Speech_to_Text_Inference_Boost_GT.sh
index b2c73ed8a34d..77a9e39c673f 100644
--- a/tests/functional_tests/L2_Speech_Transcription_Speech_to_Text_Inference_Boost_GT.sh
+++ b/tests/functional_tests/L2_Speech_Transcription_Speech_to_Text_Inference_Boost_GT.sh
@@ -42,3 +42,18 @@ coverage run -a --data-file=/workspace/.coverage --source=/workspace/nemo \
     enable_itn=False \
     enable_nmt=False \
     asr_output_granularity=segment
+
+# Cache-Aware RNN-T model
+coverage run -a --data-file=/workspace/.coverage --source=/workspace/nemo \
+    examples/asr/asr_streaming_inference/asr_streaming_infer.py \
+    --config-path="../conf/asr_streaming_inference/" \
+    --config-name=cache_aware_rnnt.yaml \
+    audio_file="/home/TestData/asr/canary/dev-other-wav-10-boost-gt.json" \
+    output_filename="/tmp/stt_inference_boost_gt_res_ca_rnnt.json" \
+    asr.model_name="nvidia/nemotron-speech-streaming-en-0.6b" \
+    streaming.batch_size=5 \
+    lang=en \
+    enable_pnc=False \
+    enable_itn=False \
+    enable_nmt=False \
+    asr_output_granularity=segment