diff --git a/src/fairseq2/optim/dynamic_loss_scaler.py b/src/fairseq2/optim/dynamic_loss_scaler.py
index cc8c628e3..71703cad5 100644
--- a/src/fairseq2/optim/dynamic_loss_scaler.py
+++ b/src/fairseq2/optim/dynamic_loss_scaler.py
@@ -32,7 +32,7 @@ class DynamicLossScaler:
     _optimizer: Optimizer
     _scale_window: int
     _min_scale: float
-    _is_enabled: bool
+    _enabled: bool
 
     # compat: consolidate into `GradScaler` once we cease support for PT2.2
     _grad_scaler: GradScaler | ShardedGradScaler
@@ -125,7 +125,7 @@ def __init__(
         self._optimizer = optimizer
         self._scale_window = scale_window
         self._min_scale = min_scale
-        self._is_enabled = enabled
+        self._enabled = enabled
 
     def state_dict(self) -> dict[str, Any]:
         return {"grad_scaler": self._grad_scaler.state_dict()}
@@ -194,7 +194,7 @@ def _are_close(a: float, b: float) -> bool:
 
     def unscale_gradients_(self) -> None:
         """Unscale the associated optimizer's gradients by the current scale."""
-        if not supports_manual_gradient_scaling(self._optimizer):
+        if self._enabled and not supports_manual_gradient_scaling(self._optimizer):
             raise RuntimeError(
                 "`optimizer` must support manual gradient scaling via `torch.cuda.amp.GradScaler`, but supports only implicit scaling in its step function (i.e. `_step_supports_amp_scaling == True`)."
             )
@@ -212,7 +212,7 @@ def get_scale(self) -> float:
     @property
     def is_enabled(self) -> bool:
         """``True`` if the loss scaling is enabled."""
-        return self._is_enabled
+        return self._enabled
 
 
 @final
diff --git a/src/fairseq2/recipes/evaluator.py b/src/fairseq2/recipes/evaluator.py
index 37e0fde06..2d5f85a19 100644
--- a/src/fairseq2/recipes/evaluator.py
+++ b/src/fairseq2/recipes/evaluator.py
@@ -8,6 +8,7 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
+from contextlib import AbstractContextManager, nullcontext
 from itertools import count
 from pathlib import Path
 from typing import Generic, TypeVar, final
@@ -29,7 +30,7 @@
 )
 from fairseq2.recipes.common_metrics import extend_batch_metrics
 from fairseq2.recipes.utils.cli import create_rich_progress
-from fairseq2.typing import CPU
+from fairseq2.typing import CPU, DataType
 from fairseq2.utils.profiler import Stopwatch
 from fairseq2.utils.rng import RngBag
 
@@ -104,6 +105,8 @@ class Evaluator(Generic[BatchT]):
     _root_gang: Gang
     _dp_gang: Gang
     _tp_gang: Gang
+    _dtype: DataType
+    _amp: bool
     _metric_recorders: list[MetricRecorder]
     _seed: int
     _wall_watch: Stopwatch
@@ -118,6 +121,8 @@ def __init__(
         wall_watch: Stopwatch,
         dp_gang: Gang | None = None,
         tp_gang: Gang | None = None,
+        dtype: DataType = torch.float32,
+        amp: bool = False,
         tb_dir: Path | None = None,
         metrics_dir: Path | None = None,
         seed: int = 2,
@@ -135,6 +140,10 @@ def __init__(
             The data parallel gang. If ``None``, ``root_gang`` will be used.
         :param tp_gang:
             The tensor parallel gang. Only required for tensor parallel models.
+        :param dtype:
+            The data type of the model.
+        :param amp:
+            If ``True``, enables ``torch.amp``.
         :param tb_dir:
             The TensorBoard log directory to dump metrics.
         :param metrics_dir:
@@ -168,6 +177,10 @@ def __init__(
                     f"The coordinator process of `root_gang` (i.e. rank 0) must be rank 0 in `dp_gang` and `tp_gang`, but is {self._dp_gang.rank} and {self._tp_gang.rank} instead."
                 )
 
+        self._dtype = dtype
+
+        self._amp = amp
+
         if root_gang.rank == 0:
             self._metric_recorders = [LogMetricRecorder(log)]
 
@@ -239,12 +252,19 @@ def _evaluate_unit(
                     break
 
                 for batch in batches:
-                    unit(batch)
+                    with self._maybe_autocast():
+                        unit(batch)
 
                 num_effective_batches += 1
 
         self._publish_metrics(unit, num_effective_batches, watch.get_elapsed_time())
 
+    def _maybe_autocast(self) -> AbstractContextManager[None]:
+        if self._dtype == torch.float32 or not self._amp:
+            return nullcontext()
+
+        return torch.autocast(device_type=self._dp_gang.device.type, dtype=self._dtype)
+
     def _publish_metrics(
         self, unit: EvalUnit[BatchT], num_batches: int, elapsed_time: float
     ) -> None:
diff --git a/src/fairseq2/recipes/generator.py b/src/fairseq2/recipes/generator.py
index af1500561..5270a52ff 100644
--- a/src/fairseq2/recipes/generator.py
+++ b/src/fairseq2/recipes/generator.py
@@ -7,6 +7,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from contextlib import AbstractContextManager, nullcontext
 from itertools import count
 from pathlib import Path
 from typing import Generic, TypeVar, final
@@ -27,7 +28,7 @@
 )
 from fairseq2.recipes.common_metrics import extend_batch_metrics
 from fairseq2.recipes.utils.cli import create_rich_progress
-from fairseq2.typing import CPU
+from fairseq2.typing import CPU, DataType
 from fairseq2.utils.profiler import Stopwatch
 from fairseq2.utils.rng import RngBag
 
@@ -79,6 +80,8 @@ class Generator(Generic[BatchT]):
     _root_gang: Gang
     _dp_gang: Gang
     _tp_gang: Gang
+    _dtype: DataType
+    _amp: bool
     _metric_recorders: list[MetricRecorder]
     _seed: int
     _wall_watch: Stopwatch
@@ -93,6 +96,8 @@ def __init__(
         wall_watch: Stopwatch,
         dp_gang: Gang | None = None,
         tp_gang: Gang | None = None,
+        dtype: DataType = torch.float32,
+        amp: bool = False,
         metrics_dir: Path | None = None,
         seed: int = 2,
     ) -> None:
@@ -109,6 +114,10 @@ def __init__(
             The data parallel gang. If ``None``, ``gang`` will be used.
         :param tp_gang:
             The tensor parallel gang. Only required for tensor parallel models.
+        :param dtype:
+            The data type of the model.
+        :param amp:
+            If ``True``, enables ``torch.amp``.
         :param metrics_dir:
             The directory to dump metrics.
         :param seed:
@@ -135,6 +144,10 @@ def __init__(
                     f"The coordinator process of `root_gang` (i.e. rank 0) must be rank 0 in `dp_gang` and `tp_gang`, but is {self._dp_gang.rank} and {self._tp_gang.rank} instead."
                 )
 
+        self._dtype = dtype
+
+        self._amp = amp
+
         if root_gang.rank == 0:
             self._metric_recorders = [LogMetricRecorder(log)]
 
@@ -194,12 +207,19 @@ def _do_run(self) -> None:
                     break
 
                 for batch in batches:
-                    self._unit(batch)
+                    with self._maybe_autocast():
+                        self._unit(batch)
 
                 num_effective_batches += 1
 
         self._publish_metrics(num_effective_batches, watch.get_elapsed_time())
 
+    def _maybe_autocast(self) -> AbstractContextManager[None]:
+        if self._dtype == torch.float32 or not self._amp:
+            return nullcontext()
+
+        return torch.autocast(device_type=self._dp_gang.device.type, dtype=self._dtype)
+
     def _publish_metrics(self, num_batches: int, elapsed_time: float) -> None:
         log.debug("Syncing metrics.")
 
diff --git a/src/fairseq2/recipes/lm/instruction_finetune.py b/src/fairseq2/recipes/lm/instruction_finetune.py
index f30f86ba5..1fe91dd66 100644
--- a/src/fairseq2/recipes/lm/instruction_finetune.py
+++ b/src/fairseq2/recipes/lm/instruction_finetune.py
@@ -71,7 +71,7 @@ class InstructionFinetuneConfig:
     train_split: str = "default"
     """The name of the train data split."""
 
-    valid_split: str = "valid"
+    valid_split: str | None = None
     """The name of the valid data split."""
 
     max_seq_len: int = 8192
@@ -80,7 +80,7 @@ class InstructionFinetuneConfig:
     max_num_tokens: int = 8192 * 2
     """The maximum number of tokens per batch."""
 
-    max_num_valid_tokens: int = 8192 * 2
+    max_num_valid_tokens: int | None = None
     """The maximum number of tokens per validation batch."""
 
     example_shuffle_window: int = 10_000
@@ -105,8 +105,14 @@ class InstructionFinetuneConfig:
     dtype: DataType = torch.bfloat16
     """The data type of the model."""
 
-    mixed_precision: bool = True
-    """If ``True``, the model will be trained in mixed precision."""
+    mixed_precision: Literal["none", "static", "dynamic"] = "static"
+    """
+    If 'none', the whole training will be run in `dtype`. If 'static', forward
+    and backward passes will be run in `dtype`, but the optimizer step will be
+    run in full precision. If 'dynamic', forward and backward passes will be run
+    with `torch.amp` in `dtype`, but the optimizer step will be run in full
+    precision.
+    """
 
     data_parallelism: Literal["ddp", "fsdp"] = "fsdp"
     """The data parallelism API to use."""
@@ -161,9 +167,6 @@ class InstructionFinetuneConfig:
     max_num_data_epochs: int | None = None
     """The maximum number of data epochs to train for."""
 
-    validate: bool = False
-    """If ``True``, runs validation."""
-
     validate_after_n_steps: int = 0
     """The number of steps after which to start validating the model."""
 
@@ -323,7 +326,7 @@ def load_instruction_finetuner(
 
     init_device = META
 
-    dtype = torch.float32 if config.mixed_precision else config.dtype
+    dtype = config.dtype if config.mixed_precision == "none" else torch.float32
 
     has_checkpoint = checkpoint_manager.has_checkpoint()
 
@@ -372,6 +375,8 @@ def load_instruction_finetuner(
 
     checkpoint_manager.save_model_metadata(base_asset=model_card.name)
 
+    mp_dtype = config.dtype if config.mixed_precision == "static" else None
+
     dp_model = to_data_parallel(
         model,
         dp_gang,
@@ -379,7 +384,7 @@ def load_instruction_finetuner(
         log,
         fsdp_broadcast_state=not has_checkpoint,
         fsdp_reshard_after_forward=config.fsdp_reshard_after_forward,
-        fsdp_mixed_precision_dtype=config.dtype if config.mixed_precision else None,
+        fsdp_mixed_precision_dtype=mp_dtype,
         fsdp_fp32_reduce=True,
         fsdp_wrap_granularity=config.fsdp_wrap_granularity,
     )
@@ -447,16 +452,18 @@ def load_instruction_finetuner(
         ) from ex
 
     # Initialize the validation unit.
-    if config.validate:
+    if config.valid_split is not None:
         valid_unit = InstructionValidUnit(criterion, dp_gang)
 
+        max_num_tokens = config.max_num_valid_tokens or config.max_num_tokens
+
         try:
             valid_data_reader = dataset.create_reader(
                 config.valid_split,
                 tokenizer,
                 dp_gang,
                 config.max_seq_len,
-                batching=LengthBatching(config.max_num_valid_tokens),
+                batching=LengthBatching(max_num_tokens),
                 example_shuffle_window=config.example_shuffle_window,
                 batch_shuffle_window=config.batch_shuffle_window,
                 sync_mode="until_last",
@@ -479,6 +486,12 @@ def load_instruction_finetuner(
 
     seed += 1
 
+    # TODO: Fix once we support static mixed precision on one device.
+    if config.mixed_precision == "static":
+        amp = root_gang.size == 1 or config.data_parallelism != "fsdp"
+    else:
+        amp = config.mixed_precision == "dynamic"
+
     # Initialize the trainer.
     return Trainer[SequenceBatch](
         unit=unit,
@@ -489,9 +502,9 @@ def load_instruction_finetuner(
         dtype=config.dtype,
         optimizer=optimizer,
         lr_scheduler=lr_scheduler,
-        amp=config.mixed_precision,
         fp16_loss_scale=config.fp16_loss_scale,
         max_gradient_norm=config.max_gradient_norm,
+        amp=amp,
         max_num_steps=config.max_num_steps,
         max_num_data_epochs=config.max_num_data_epochs,
         valid_units=valid_units,
diff --git a/src/fairseq2/recipes/lm/preference_finetune/recipe.py b/src/fairseq2/recipes/lm/preference_finetune/recipe.py
index f99b35252..e7ff79638 100644
--- a/src/fairseq2/recipes/lm/preference_finetune/recipe.py
+++ b/src/fairseq2/recipes/lm/preference_finetune/recipe.py
@@ -91,8 +91,14 @@ class PreferenceOptimizationConfig:
     dtype: DataType = torch.bfloat16
     """The data type of the model."""
 
-    mixed_precision: bool = True
-    """If ``True``, the model will be trained in mixed precision."""
+    mixed_precision: Literal["none", "static", "dynamic"] = "static"
+    """
+    If 'none', the whole training will be run in `dtype`. If 'static', forward
+    and backward passes will be run in `dtype`, but the optimizer step will be
+    run in full precision. If 'dynamic', forward and backward passes will be run
+    with `torch.amp` in `dtype`, but the optimizer step will be run in full
+    precision.
+    """
 
     data_parallelism: Literal["ddp", "fsdp"] = "fsdp"
     """The data parallelism API to use."""
@@ -289,7 +295,7 @@ def load_preference_finetuner(
 
     init_device = META
 
-    dtype = torch.float32 if config.mixed_precision else config.dtype
+    dtype = config.dtype if config.mixed_precision == "none" else torch.float32
 
     has_checkpoint = checkpoint_manager.has_checkpoint()
 
@@ -333,6 +339,8 @@ def load_preference_finetuner(
 
     checkpoint_manager.save_model_metadata(base_asset=model_card.name)
 
+    mp_dtype = config.dtype if config.mixed_precision == "static" else None
+
     dp_model = to_data_parallel(
         model,
         dp_gang,
@@ -340,7 +348,7 @@ def load_preference_finetuner(
         log,
         fsdp_broadcast_state=not has_checkpoint,
         fsdp_reshard_after_forward=config.fsdp_reshard_after_forward,
-        fsdp_mixed_precision_dtype=config.dtype if config.mixed_precision else None,
+        fsdp_mixed_precision_dtype=mp_dtype,
         fsdp_fp32_reduce=True,
         fsdp_wrap_granularity=config.fsdp_wrap_granularity,
     )
@@ -422,6 +430,12 @@ def load_preference_finetuner(
             "The learning rate scheduler cannot be created. See nested exception for details."
         ) from ex
 
+    # TODO: Fix once we support static mixed precision on one device.
+    if config.mixed_precision == "static":
+        amp = root_gang.size == 1 or config.data_parallelism != "fsdp"
+    else:
+        amp = config.mixed_precision == "dynamic"
+
     # Initialize the trainer.
     return Trainer[PreferenceOptimizationBatch](
         unit=unit,
@@ -432,9 +446,9 @@ def load_preference_finetuner(
         dtype=config.dtype,
         optimizer=optimizer,
         lr_scheduler=lr_scheduler,
-        amp=config.mixed_precision,
         fp16_loss_scale=config.fp16_loss_scale,
         max_gradient_norm=config.max_gradient_norm,
+        amp=amp,
         max_num_steps=config.max_num_steps,
         max_num_data_epochs=config.max_num_data_epochs,
         checkpoint_manager=checkpoint_manager,
diff --git a/src/fairseq2/recipes/lm/text_generate.py b/src/fairseq2/recipes/lm/text_generate.py
index 188c59499..ca99890cf 100644
--- a/src/fairseq2/recipes/lm/text_generate.py
+++ b/src/fairseq2/recipes/lm/text_generate.py
@@ -75,6 +75,9 @@ class TextGenerateConfig:
     dtype: DataType = torch.bfloat16
     """The data type of the model."""
 
+    amp: bool = False
+    """If ``True``, runs evaluation with ``torch.amp``."""
+
     tensor_parallel_size: int = 1
     """The size of tensor parallelism."""
 
@@ -301,6 +304,8 @@ def load_text_generator(
         root_gang=root_gang,
         dp_gang=dp_gang,
         tp_gang=tp_gang,
+        dtype=config.dtype,
+        amp=config.amp,
         metrics_dir=output_dir.joinpath("metrics"),
         seed=seed,
         wall_watch=wall_watch,
diff --git a/src/fairseq2/recipes/mt/eval.py b/src/fairseq2/recipes/mt/eval.py
index a06c89a0f..ef6236b3b 100644
--- a/src/fairseq2/recipes/mt/eval.py
+++ b/src/fairseq2/recipes/mt/eval.py
@@ -81,6 +81,9 @@ class MTEvalConfig:
     dtype: DataType = torch.float16
     """The data type of the model."""
 
+    amp: bool = False
+    """If ``True``, runs evaluation with ``torch.amp``."""
+
     # Loss
     label_smoothing: float = 0.1
     """The amount of label smoothing to apply while computing the loss."""
@@ -308,6 +311,8 @@ def load_mt_evaluator(
         units=units,
         data_readers=data_readers,
         root_gang=gang,
+        dtype=config.dtype,
+        amp=config.amp,
         tb_dir=output_dir.joinpath("tb"),
         metrics_dir=output_dir.joinpath("metrics"),
         seed=seed,
diff --git a/src/fairseq2/recipes/mt/train.py b/src/fairseq2/recipes/mt/train.py
index 787e02a2e..d6b0ab01e 100644
--- a/src/fairseq2/recipes/mt/train.py
+++ b/src/fairseq2/recipes/mt/train.py
@@ -424,6 +424,9 @@ def load_mt_trainer(config: MTTrainConfig, output_dir: Path) -> Trainer[Seq2SeqB
 
             valid_data_readers.append(valid_data_reader)
 
+    # TODO: Fix once we support static mixed precision on one device.
+    amp = gang.size == 1 or config.data_parallelism != "fsdp"
+
     # Initialize the trainer.
     return Trainer[Seq2SeqBatch](
         unit=unit,
@@ -434,6 +437,7 @@ def load_mt_trainer(config: MTTrainConfig, output_dir: Path) -> Trainer[Seq2SeqB
         lr_scheduler=lr_scheduler,
         fp16_loss_scale=config.fp16_loss_scale,
         max_gradient_norm=config.max_gradient_norm,
+        amp=amp,
         max_num_steps=config.max_num_steps,
         max_num_data_epochs=config.max_num_data_epochs,
         score_metric_name="chrf" if config.compute_bleu_chrf else None,
diff --git a/src/fairseq2/recipes/mt/translate.py b/src/fairseq2/recipes/mt/translate.py
index 48815f823..ed788cf3d 100644
--- a/src/fairseq2/recipes/mt/translate.py
+++ b/src/fairseq2/recipes/mt/translate.py
@@ -78,6 +78,9 @@ class TextTranslateConfig:
     dtype: DataType = torch.float16
     """The data type of the model."""
 
+    amp: bool = False
+    """If ``True``, runs evaluation with ``torch.amp``."""
+
     # Generation
     generator: str = "beam_search"
     """The sequence generator."""
@@ -245,6 +248,8 @@ def load_text_translator(
         unit=unit,
         data_reader=data_reader,
         root_gang=gang,
+        dtype=config.dtype,
+        amp=config.amp,
         metrics_dir=output_dir.joinpath("metrics"),
         seed=seed,
         wall_watch=wall_watch,
diff --git a/src/fairseq2/recipes/trainer.py b/src/fairseq2/recipes/trainer.py
index 71ca17e77..3491e15e1 100644
--- a/src/fairseq2/recipes/trainer.py
+++ b/src/fairseq2/recipes/trainer.py
@@ -124,6 +124,7 @@ class Trainer(StatefulObjectBag, Generic[BatchT]):
     _lr_scheduler: LRScheduler
     _loss_scaler: DynamicLossScaler
     _max_gradient_norm: float | None
+    _amp: bool
     _step_nr: int
     _max_num_steps: int | None
     _data_epoch_nr: int
@@ -179,13 +180,13 @@ def __init__(
         optimizer: Optimizer,
         checkpoint_manager: CheckpointManager,
         wall_watch: Stopwatch,
-        dtype: DataType = torch.float32,
         dp_gang: Gang | None = None,
         tp_gang: Gang | None = None,
+        dtype: DataType = torch.float32,
         lr_scheduler: LRScheduler | None = None,
-        amp: bool = True,
         fp16_loss_scale: tuple[float, float] = (128.0, 0.0001),
         max_gradient_norm: float | None = None,
+        amp: bool = False,
         max_num_steps: int | None = None,
         max_num_data_epochs: int | None = None,
         score_metric_name: str | None = None,
@@ -229,7 +230,7 @@ def __init__(
         :param wall_watch:
             The stopwatch to track process wall-time.
         :param dtype:
-            The data type to train with.
+            The data type of the model.
         :param dp_gang:
             The data parallel gang. If ``None``, ``gang`` will be used.
         :param tp_gang:
@@ -237,9 +238,7 @@ def __init__(
         :param lr_scheduler:
             The learning rate scheduler.
         :param amp:
-            If ``True``, enables automatic mixed precision (i.e. ``torch.amp``).
-            If the model is trained with mixed precision DDP or FSDP, it takes
-            precedence over this setting and no autocast will be applied.
+            If ``True``, enables ``torch.amp``.
         :param fp16_loss_scale:
             The initial and minimum loss scale for fp16 training.
         :param max_gradient_norm:
@@ -345,8 +344,6 @@ def __init__(
 
         self._lr_scheduler = lr_scheduler or NoopLR(optimizer)
 
-        self._amp = amp
-
         fp16_init_scale, fp16_min_scale = fp16_loss_scale
 
         self._loss_scaler = DynamicLossScaler(
@@ -361,6 +358,8 @@ def __init__(
 
         self._max_gradient_norm = max_gradient_norm
 
+        self._amp = amp
+
         self.register_stateful("_step_nr", 0)
 
         if max_num_steps == 0:
@@ -832,11 +831,6 @@ def _maybe_autocast(self) -> AbstractContextManager[None]:
         if self._dtype == torch.float32 or not self._amp:
             return nullcontext()
 
-        if self._model.training and isinstance(self._model, (DDP, FSDP)):
-            mp = self._model.mixed_precision
-            if mp is not None and mp.param_dtype is not None:
-                return nullcontext()
-
         return torch.autocast(device_type=self._dp_gang.device.type, dtype=self._dtype)
 
     def _should_publish_metrics(self) -> bool:
@@ -895,9 +889,9 @@ def _should_validate(self) -> bool:
     def _validate(self) -> None:
         log.info("Starting validation after step {}.", self._step_nr)
 
-        with summon_fsdp_for_validation(self._model):
-            self._model.eval()
+        self._model.eval()
 
+        with summon_fsdp_for_validation(self._model):
             unit_scores = []
 
             for unit, data_reader in zip(self._valid_units, self._valid_data_readers):
@@ -910,7 +904,7 @@ def _validate(self) -> None:
 
             self._valid_score = self._compute_valid_score(unit_scores)
 
-            self._model.train()
+        self._model.train()
 
         log.info("Validation complete.")
 
diff --git a/src/fairseq2/recipes/utils/sweep.py b/src/fairseq2/recipes/utils/sweep.py
index e1149c0d2..5db10cf74 100644
--- a/src/fairseq2/recipes/utils/sweep.py
+++ b/src/fairseq2/recipes/utils/sweep.py
@@ -74,6 +74,7 @@ def __init__(self, *, allow_set: set[object] | None = None) -> None:
                 "max_num_steps",
                 "max_num_tokens",
                 "max_seq_len",
+                "mixed_precision",
                 "model",
                 "model_arch",
                 "model_config",
diff --git a/src/fairseq2/recipes/wav2vec2/asr/eval.py b/src/fairseq2/recipes/wav2vec2/asr/eval.py
index 6017e0971..2aea92c49 100644
--- a/src/fairseq2/recipes/wav2vec2/asr/eval.py
+++ b/src/fairseq2/recipes/wav2vec2/asr/eval.py
@@ -80,6 +80,9 @@ class Wav2Vec2AsrEvalConfig:
     dtype: DataType = torch.float16
     """The data type of the model."""
 
+    amp: bool = False
+    """If ``True``, runs evaluation with ``torch.amp``."""
+
     # Misc
     seed: int = 2
     """The random number generator seed to use."""
@@ -231,6 +234,8 @@ def load_wav2vec2_asr_evaluator(
         units=[unit],
         data_readers=[data_reader],
         root_gang=gang,
+        dtype=config.dtype,
+        amp=config.amp,
         tb_dir=output_dir.joinpath("tb"),
         metrics_dir=output_dir.joinpath("metrics"),
         seed=seed,
diff --git a/src/fairseq2/recipes/wav2vec2/asr/train.py b/src/fairseq2/recipes/wav2vec2/asr/train.py
index 7bf4bfc40..b5550b837 100644
--- a/src/fairseq2/recipes/wav2vec2/asr/train.py
+++ b/src/fairseq2/recipes/wav2vec2/asr/train.py
@@ -465,6 +465,9 @@ def load_wav2vec2_asr_trainer(
 
     seed += 1
 
+    # TODO: Fix once we support static mixed precision on one device.
+    amp = gang.size == 1 or config.data_parallelism != "fsdp"
+
     # Initialize the trainer.
     return Trainer[Seq2SeqBatch](
         unit=unit,
@@ -475,6 +478,7 @@ def load_wav2vec2_asr_trainer(
         lr_scheduler=lr_scheduler,
         fp16_loss_scale=config.fp16_loss_scale,
         max_gradient_norm=config.max_gradient_norm,
+        amp=amp,
         max_num_steps=config.max_num_steps,
         max_num_data_epochs=config.max_num_data_epochs,
         score_metric_name="wer",
diff --git a/src/fairseq2/recipes/wav2vec2/eval.py b/src/fairseq2/recipes/wav2vec2/eval.py
index 179b6caa3..08ccdf61d 100644
--- a/src/fairseq2/recipes/wav2vec2/eval.py
+++ b/src/fairseq2/recipes/wav2vec2/eval.py
@@ -75,6 +75,9 @@ class Wav2Vec2EvalConfig:
     dtype: DataType = torch.float16
     """The data type of the model."""
 
+    amp: bool = False
+    """If ``True``, runs evaluation with ``torch.amp``."""
+
     # Loss
     diversity_loss_weight: float = 0.1
     """The weight of the diversity loss."""
@@ -199,6 +202,8 @@ def load_wav2vec2_evaluator(
         units=[unit],
         data_readers=[data_reader],
         root_gang=gang,
+        dtype=config.dtype,
+        amp=config.amp,
         tb_dir=output_dir.joinpath("tb"),
         metrics_dir=output_dir.joinpath("metrics"),
         seed=seed,
diff --git a/src/fairseq2/recipes/wav2vec2/train.py b/src/fairseq2/recipes/wav2vec2/train.py
index 6391a5f92..124b88e11 100644
--- a/src/fairseq2/recipes/wav2vec2/train.py
+++ b/src/fairseq2/recipes/wav2vec2/train.py
@@ -368,6 +368,9 @@ def load_wav2vec2_trainer(
 
     seed += 1
 
+    # TODO: Fix once we support static mixed precision on one device.
+    amp = gang.size == 1 or config.data_parallelism != "fsdp"
+
     # Initialize the trainer.
     return Trainer[SequenceBatch](
         unit=unit,
@@ -378,6 +381,7 @@ def load_wav2vec2_trainer(
         lr_scheduler=lr_scheduler,
         fp16_loss_scale=config.fp16_loss_scale,
         max_gradient_norm=config.max_gradient_norm,
+        amp=amp,
         max_num_steps=config.max_num_steps,
         max_num_data_epochs=config.max_num_data_epochs,
         score_metric_name="loss",