diff --git a/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py b/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py
index 786c953b0fdf..0e1ec5f566a0 100644
--- a/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py
+++ b/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py
@@ -612,20 +612,29 @@ def sample(
             generator: Optional[torch.Generator] = None,
             group_metadata: StrategyMetadata | None = None,
         ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-            logits = self._prepare_logits_with_temperature(
-                logits, group_logit_indices, self._temperature
-            )
-            new_tokens = flashinfer.sampling.sampling_from_logits(
+            new_tokens, _ = self._sample_with_probs(
                 logits,
-                # NB: Leveraging 'indices' would require applying temperature+softmax before batching,
-                #     because 'flashinfer.sampling.softmax' has no 'indices' argument; but that would
-                #     compute unnecessarily softmax also for situations allowing
-                #     flashinfer.sampling...._sampling_from_logits.
-                # indices=group_logit_indices,
-                deterministic=True,
+                group_logit_indices=group_logit_indices,
+                top_k=None,
+                top_p=None,
+                temperature=self._temperature,
                 generator=generator,
-                check_nan=self._flashinfer_check_nans(logits),
             )
+            # FIXME: https://nvbugs/5791242
+            # logits = self._prepare_logits_with_temperature(
+            #    logits, group_logit_indices, self._temperature
+            # )
+            # new_tokens = flashinfer.sampling.sampling_from_logits(
+            #    logits,
+            #    # NB: Leveraging 'indices' would require applying temperature+softmax before batching,
+            #    #     because 'flashinfer.sampling.softmax' has no 'indices' argument; but that would
+            #    #     compute unnecessarily softmax also for situations allowing
+            #    #     flashinfer.sampling...._sampling_from_logits.
+            #    # indices=group_logit_indices,
+            #    deterministic=True,
+            #    generator=generator,
+            #    check_nan=self._flashinfer_check_nans(logits),
+            # )
             return new_tokens, None
 
     class BeamSearchSampleOnly(BeamSearchMixin, StrategyImplSampleOnly):