diff --git a/haystack/components/agents/agent.py b/haystack/components/agents/agent.py
index b5e5b1a1e4..68c9fc0907 100644
--- a/haystack/components/agents/agent.py
+++ b/haystack/components/agents/agent.py
@@ -554,7 +554,6 @@ def run(  # noqa: PLR0915
             - "messages": List of all messages exchanged during the agent's run.
             - "last_message": The last message exchanged during the agent's run.
             - Any additional keys defined in the `state_schema`.
-        :raises RuntimeError: If the Agent component wasn't warmed up before calling `run()`.
         :raises BreakpointException: If an agent breakpoint is triggered.
         """
         # We pop parent_snapshot from kwargs to avoid passing it into State.
@@ -722,7 +721,6 @@ async def run_async(
             - "messages": List of all messages exchanged during the agent's run.
             - "last_message": The last message exchanged during the agent's run.
             - Any additional keys defined in the `state_schema`.
-        :raises RuntimeError: If the Agent component wasn't warmed up before calling `run_async()`.
         :raises BreakpointException: If an agent breakpoint is triggered.
         """
         # We pop parent_snapshot from kwargs to avoid passing it into State.
diff --git a/haystack/components/audio/whisper_local.py b/haystack/components/audio/whisper_local.py
index 66bf2a3d0d..cb1d332b99 100644
--- a/haystack/components/audio/whisper_local.py
+++ b/haystack/components/audio/whisper_local.py
@@ -127,9 +127,7 @@ def run(self, sources: list[Union[str, Path, ByteStream]], whisper_params: Optio
                 for the transcription.
         """
         if self._model is None:
-            raise RuntimeError(
-                "The component LocalWhisperTranscriber was not warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if whisper_params is None:
             whisper_params = self.whisper_params
@@ -158,6 +156,19 @@ def transcribe(self, sources: list[Union[str, Path, ByteStream]], **kwargs) -> l
             documents.append(doc)
         return documents
 
+    def _get_path(self, source: Union[str, Path, ByteStream]) -> Path:
+        if isinstance(source, (Path, str)):
+            return Path(source)
+
+        potential_path = source.meta.get("file_path")
+        if potential_path is not None:
+            return Path(potential_path)
+        else:
+            with tempfile.NamedTemporaryFile(delete=False) as fp:
+                path = Path(fp.name)
+                source.to_file(path)
+            return path
+
     def _raw_transcribe(self, sources: list[Union[str, Path, ByteStream]], **kwargs) -> dict[Path, Any]:
         """
         Transcribes the given audio files. Returns the output of the model, a dictionary, for each input file.
@@ -172,20 +183,16 @@ def _raw_transcribe(self, sources: list[Union[str, Path, ByteStream]], **kwargs)
             A dictionary mapping 'file_path' to 'transcription'.
         """
         if self._model is None:
-            raise RuntimeError("Model is not loaded, please run 'warm_up()' before calling 'run()'")
+            self.warm_up()
 
         return_segments = kwargs.pop("return_segments", False)
         transcriptions = {}
 
         for source in sources:
-            path = Path(source) if not isinstance(source, ByteStream) else source.meta.get("file_path")
-
-            if isinstance(source, ByteStream) and path is None:
-                with tempfile.NamedTemporaryFile(delete=False) as fp:
-                    path = Path(fp.name)
-                    source.to_file(path)
+            path = self._get_path(source)
 
-            transcription = self._model.transcribe(str(path), **kwargs)
+            # mypy doesn't know this is set in warm_up
+            transcription = self._model.transcribe(str(path), **kwargs)  # type: ignore[attr-defined]
 
             if not return_segments:
                 transcription.pop("segments", None)
diff --git a/haystack/components/classifiers/zero_shot_document_classifier.py b/haystack/components/classifiers/zero_shot_document_classifier.py
index 94794e816b..0d1d788cd4 100644
--- a/haystack/components/classifiers/zero_shot_document_classifier.py
+++ b/haystack/components/classifiers/zero_shot_document_classifier.py
@@ -203,10 +203,7 @@ def run(self, documents: list[Document], batch_size: int = 1):
         """
 
         if self.pipeline is None:
-            raise RuntimeError(
-                "The component TransformerZeroShotDocumentClassifier wasn't warmed up. "
-                "Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
             raise TypeError(
@@ -231,7 +228,10 @@ def run(self, documents: list[Document], batch_size: int = 1):
             for doc in documents
         ]
 
-        predictions = self.pipeline(texts, self.labels, multi_label=self.multi_label, batch_size=batch_size)
+        # mypy doesn't know this is set in warm_up
+        predictions = self.pipeline(  # type: ignore[misc]
+            texts, self.labels, multi_label=self.multi_label, batch_size=batch_size
+        )
 
         new_documents = []
         for prediction, document in zip(predictions, documents):
diff --git a/haystack/components/embedders/image/sentence_transformers_doc_image_embedder.py b/haystack/components/embedders/image/sentence_transformers_doc_image_embedder.py
index a78eb9bab8..f174cd3f25 100644
--- a/haystack/components/embedders/image/sentence_transformers_doc_image_embedder.py
+++ b/haystack/components/embedders/image/sentence_transformers_doc_image_embedder.py
@@ -237,7 +237,7 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]]:
                 "In case you want to embed a string, please use the SentenceTransformersTextEmbedder."
             )
         if self._embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
 
         images_source_info = _extract_image_sources_info(
             documents=documents, file_path_meta_field=self.file_path_meta_field, root_path=self.root_path
@@ -270,7 +270,8 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]]:
         if none_images_doc_ids:
             raise RuntimeError(f"Conversion failed for some documents. Document IDs: {none_images_doc_ids}.")
 
-        embeddings = self._embedding_backend.embed(
+        # mypy doesn't know this is set in warm_up
+        embeddings = self._embedding_backend.embed(  # type: ignore[union-attr]
             data=images_to_embed,
             batch_size=self.batch_size,
             show_progress_bar=self.progress_bar,
diff --git a/haystack/components/embedders/sentence_transformers_document_embedder.py b/haystack/components/embedders/sentence_transformers_document_embedder.py
index de92d8184b..3ed1e7b12a 100644
--- a/haystack/components/embedders/sentence_transformers_document_embedder.py
+++ b/haystack/components/embedders/sentence_transformers_document_embedder.py
@@ -22,7 +22,7 @@ class SentenceTransformersDocumentEmbedder:
     It stores the embeddings in the `embedding` metadata field of each document.
     You can also embed documents' metadata.
     Use this component in indexing pipelines to embed input documents
-    and send them to DocumentWriter to write a into a Document Store.
+    and send them to DocumentWriter to write into a Document Store.
 
     ### Usage example:
 
@@ -244,7 +244,7 @@ def run(self, documents: list[Document]):
                 "In case you want to embed a string, please use the SentenceTransformersTextEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
 
         texts_to_embed = []
         for doc in documents:
@@ -256,7 +256,8 @@ def run(self, documents: list[Document]):
             )
             texts_to_embed.append(text_to_embed)
 
-        embeddings = self.embedding_backend.embed(
+        # # mypy doesn't know this is set in warm_up
+        embeddings = self.embedding_backend.embed(  # type: ignore[union-attr]
             texts_to_embed,
             batch_size=self.batch_size,
             show_progress_bar=self.progress_bar,
diff --git a/haystack/components/embedders/sentence_transformers_sparse_document_embedder.py b/haystack/components/embedders/sentence_transformers_sparse_document_embedder.py
index cc48574730..22b087254d 100644
--- a/haystack/components/embedders/sentence_transformers_sparse_document_embedder.py
+++ b/haystack/components/embedders/sentence_transformers_sparse_document_embedder.py
@@ -216,7 +216,7 @@ def run(self, documents: list[Document]):
                 "In case you want to embed a list of strings, please use the SentenceTransformersSparseTextEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
 
         texts_to_embed = []
         for doc in documents:
@@ -228,7 +228,8 @@ def run(self, documents: list[Document]):
             )
             texts_to_embed.append(text_to_embed)
 
-        embeddings = self.embedding_backend.embed(
+        # mypy doesn't know this is set in warm_up
+        embeddings = self.embedding_backend.embed(  # type: ignore[union-attr]
             data=texts_to_embed, batch_size=self.batch_size, show_progress_bar=self.progress_bar
         )
 
diff --git a/haystack/components/embedders/sentence_transformers_sparse_text_embedder.py b/haystack/components/embedders/sentence_transformers_sparse_text_embedder.py
index ab8da5f0e0..314ba09eab 100644
--- a/haystack/components/embedders/sentence_transformers_sparse_text_embedder.py
+++ b/haystack/components/embedders/sentence_transformers_sparse_text_embedder.py
@@ -192,10 +192,11 @@ def run(self, text: str):
                 "SentenceTransformersSparseDocumentEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
 
         text_to_embed = self.prefix + text + self.suffix
 
-        sparse_embedding = self.embedding_backend.embed(data=[text_to_embed])[0]
+        # mypy doesn't know this is set in warm_up
+        sparse_embedding = self.embedding_backend.embed(data=[text_to_embed])[0]  # type: ignore[union-attr]
 
         return {"sparse_embedding": sparse_embedding}
diff --git a/haystack/components/embedders/sentence_transformers_text_embedder.py b/haystack/components/embedders/sentence_transformers_text_embedder.py
index da1e7a4b38..4924daeaf3 100644
--- a/haystack/components/embedders/sentence_transformers_text_embedder.py
+++ b/haystack/components/embedders/sentence_transformers_text_embedder.py
@@ -229,10 +229,12 @@ def run(self, text: str):
                 "In case you want to embed a list of Documents, please use the SentenceTransformersDocumentEmbedder."
             )
         if self.embedding_backend is None:
-            raise RuntimeError("The embedding model has not been loaded. Please call warm_up() before running.")
+            self.warm_up()
 
         text_to_embed = self.prefix + text + self.suffix
-        embedding = self.embedding_backend.embed(
+
+        # mypy doesn't know this is set in warm_up
+        embedding = self.embedding_backend.embed(  # type: ignore[union-attr]
             [text_to_embed],
             batch_size=self.batch_size,
             show_progress_bar=self.progress_bar,
diff --git a/haystack/components/evaluators/llm_evaluator.py b/haystack/components/evaluators/llm_evaluator.py
index 878e5636cd..367703fd75 100644
--- a/haystack/components/evaluators/llm_evaluator.py
+++ b/haystack/components/evaluators/llm_evaluator.py
@@ -108,6 +108,17 @@ def __init__(  # pylint: disable=too-many-positional-arguments
             generation_kwargs = {"response_format": {"type": "json_object"}, "seed": 42}
             self._chat_generator = OpenAIChatGenerator(generation_kwargs=generation_kwargs)
 
+        self._is_warmed_up = False
+
+    def warm_up(self):
+        """
+        Warm up the component by warming up the underlying chat generator.
+        """
+        if not self._is_warmed_up:
+            if hasattr(self._chat_generator, "warm_up"):
+                self._chat_generator.warm_up()
+            self._is_warmed_up = True
+
     @staticmethod
     def validate_init_parameters(
         inputs: list[tuple[str, type[list]]], outputs: list[str], examples: list[dict[str, Any]]
@@ -181,6 +192,9 @@ def run(self, **inputs) -> dict[str, Any]:
             Only in the case that  `raise_on_failure` is set to True and the received inputs are not lists or have
             different lengths, or if the output is not a valid JSON or doesn't contain the expected keys.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         self.validate_input_parameters(dict(self.inputs), inputs)
 
         # inputs is a dictionary with keys being input names and values being a list of input values
diff --git a/haystack/components/evaluators/sas_evaluator.py b/haystack/components/evaluators/sas_evaluator.py
index b2d7d91dbf..13bfc239b5 100644
--- a/haystack/components/evaluators/sas_evaluator.py
+++ b/haystack/components/evaluators/sas_evaluator.py
@@ -164,8 +164,7 @@ def run(self, ground_truth_answers: list[str], predicted_answers: list[str]) ->
             return {"score": 0.0, "individual_scores": [0.0]}
 
         if not self._similarity_model:
-            msg = "The model has not been initialized. Call warm_up() before running the evaluator."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         if isinstance(self._similarity_model, CrossEncoder):
             # For Cross Encoders we create a list of pairs of predictions and labels
diff --git a/haystack/components/extractors/named_entity_extractor.py b/haystack/components/extractors/named_entity_extractor.py
index 4d45e9b368..06039cc099 100644
--- a/haystack/components/extractors/named_entity_extractor.py
+++ b/haystack/components/extractors/named_entity_extractor.py
@@ -192,8 +192,7 @@ def run(self, documents: list[Document], batch_size: int = 1) -> dict[str, Any]:
             If the backend fails to process a document.
         """
         if not self._warmed_up:
-            msg = "The component NamedEntityExtractor was not warmed up. Call warm_up() before running the component."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         texts = [doc.content if doc.content is not None else "" for doc in documents]
         annotations = self._backend.annotate(texts, batch_size=batch_size)
diff --git a/haystack/components/generators/chat/fallback.py b/haystack/components/generators/chat/fallback.py
index 26f4f3a84f..0d216dc6f5 100644
--- a/haystack/components/generators/chat/fallback.py
+++ b/haystack/components/generators/chat/fallback.py
@@ -47,7 +47,7 @@ class FallbackChatGenerator:
     - Any other exception
     """
 
-    def __init__(self, chat_generators: list[ChatGenerator]):
+    def __init__(self, chat_generators: list[ChatGenerator]) -> None:
         """
         Creates an instance of FallbackChatGenerator.
 
@@ -58,6 +58,7 @@ def __init__(self, chat_generators: list[ChatGenerator]):
             raise ValueError(msg)
 
         self.chat_generators = list(chat_generators)
+        self._is_warmed_up = False
 
     def to_dict(self) -> dict[str, Any]:
         """Serialize the component, including nested chat generators when they support serialization."""
@@ -87,10 +88,15 @@ def warm_up(self) -> None:
 
         This method calls warm_up() on each underlying generator that supports it.
         """
+        if self._is_warmed_up:
+            return
+
         for gen in self.chat_generators:
             if hasattr(gen, "warm_up") and callable(gen.warm_up):
                 gen.warm_up()
 
+        self._is_warmed_up = True
+
     def _run_single_sync(  # pylint: disable=too-many-positional-arguments
         self,
         gen: Any,
@@ -133,7 +139,7 @@ def run(
         generation_kwargs: Union[dict[str, Any], None] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Union[StreamingCallbackT, None] = None,
-    ) -> dict[str, Any]:
+    ) -> dict[str, Union[list[ChatMessage], dict[str, Any]]]:
         """
         Execute chat generators sequentially until one succeeds.
 
@@ -147,6 +153,9 @@ def run(
               total_attempts, failed_chat_generators, plus any metadata from the successful generator.
         :raises RuntimeError: If all chat generators fail.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         failed: list[str] = []
         last_error: Union[BaseException, None] = None
 
@@ -186,7 +195,7 @@ async def run_async(
         generation_kwargs: Union[dict[str, Any], None] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Union[StreamingCallbackT, None] = None,
-    ) -> dict[str, Any]:
+    ) -> dict[str, Union[list[ChatMessage], dict[str, Any]]]:
         """
         Asynchronously execute chat generators sequentially until one succeeds.
 
@@ -200,6 +209,9 @@ async def run_async(
               total_attempts, failed_chat_generators, plus any metadata from the successful generator.
         :raises RuntimeError: If all chat generators fail.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         failed: list[str] = []
         last_error: Union[BaseException, None] = None
 
diff --git a/haystack/components/generators/chat/hugging_face_api.py b/haystack/components/generators/chat/hugging_face_api.py
index fb6d6dbb3b..0caf6d2eb7 100644
--- a/haystack/components/generators/chat/hugging_face_api.py
+++ b/haystack/components/generators/chat/hugging_face_api.py
@@ -436,7 +436,7 @@ def run(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invoke the text generation inference based on the provided messages and generation parameters.
 
@@ -454,6 +454,8 @@ def run(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage objects.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
 
         # update generation kwargs by merging with the default ones
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
@@ -485,7 +487,7 @@ async def run_async(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[ToolsType] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes the text generation inference based on the provided messages and generation parameters.
 
@@ -506,6 +508,8 @@ async def run_async(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage objects.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
 
         # update generation kwargs by merging with the default ones
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
diff --git a/haystack/components/generators/chat/hugging_face_local.py b/haystack/components/generators/chat/hugging_face_local.py
index 2296b4f7f1..d1661245b8 100644
--- a/haystack/components/generators/chat/hugging_face_local.py
+++ b/haystack/components/generators/chat/hugging_face_local.py
@@ -361,6 +361,9 @@ def run(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if self.pipeline is None:
+            self.warm_up()
+
         prepared_inputs = self._prepare_inputs(
             messages=messages, generation_kwargs=generation_kwargs, streaming_callback=streaming_callback, tools=tools
         )
@@ -475,6 +478,9 @@ async def run_async(
         :returns: A dictionary with the following keys:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if self.pipeline is None:
+            self.warm_up()
+
         prepared_inputs = self._prepare_inputs(
             messages=messages, generation_kwargs=generation_kwargs, streaming_callback=streaming_callback, tools=tools
         )
@@ -542,21 +548,16 @@ def _prepare_inputs(
         :param streaming_callback: An optional callable for handling streaming responses.
         :param tools: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
         :returns: A dictionary containing the prepared prompt, tokenizer, generation kwargs, and tools.
-        :raises RuntimeError: If the generation model has not been loaded.
         :raises ValueError: If both tools and streaming_callback are provided.
         """
-        if self.pipeline is None:
-            raise RuntimeError("The generation model has not been loaded. Please call warm_up() before running.")
-
         tools = tools or self.tools
         if tools and streaming_callback is not None:
             raise ValueError("Using tools and streaming at the same time is not supported. Please choose one.")
         flat_tools = flatten_tools_or_toolsets(tools)
         _check_duplicate_tool_names(flat_tools)
 
-        tokenizer = self.pipeline.tokenizer
-        # initialized text-generation/text2text-generation pipelines always have a non-None tokenizer
-        assert tokenizer is not None
+        # mypy doesn't know this is set in warm_up
+        tokenizer = self.pipeline.tokenizer  # type: ignore[union-attr]
 
         # Check and update generation parameters
         generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
@@ -577,14 +578,23 @@ def _prepare_inputs(
         stop_words = self._validate_stop_words(stop_words)
 
         # Set up stop words criteria if stop words exist
-        stop_words_criteria = StopWordsCriteria(tokenizer, stop_words, self.pipeline.device) if stop_words else None
+        stop_words_criteria = (
+            StopWordsCriteria(
+                tokenizer,  # type: ignore[arg-type]
+                stop_words,
+                self.pipeline.device,  # type: ignore[union-attr]
+            )
+            if stop_words
+            else None
+        )
         if stop_words_criteria:
             generation_kwargs["stopping_criteria"] = StoppingCriteriaList([stop_words_criteria])
 
         # convert messages to HF format
         hf_messages = [convert_message_to_hf_format(message) for message in messages]
 
-        prepared_prompt = tokenizer.apply_chat_template(
+        # mypy doesn't know tokenizer is set in warm_up
+        prepared_prompt = tokenizer.apply_chat_template(  # type: ignore[union-attr]
             hf_messages,
             tokenize=False,
             chat_template=self.chat_template,
@@ -595,8 +605,9 @@ def _prepare_inputs(
         assert isinstance(prepared_prompt, str)
 
         # Avoid some unnecessary warnings in the generation pipeline call
+        # mypy doesn't know tokenizer is set in warm_up
         generation_kwargs["pad_token_id"] = (
-            generation_kwargs.get("pad_token_id", tokenizer.pad_token_id) or tokenizer.eos_token_id
+            generation_kwargs.get("pad_token_id", tokenizer.pad_token_id) or tokenizer.eos_token_id  # type: ignore[union-attr]
         )
 
         return {
diff --git a/haystack/components/generators/chat/openai.py b/haystack/components/generators/chat/openai.py
index cd227e7aba..3c06df1112 100644
--- a/haystack/components/generators/chat/openai.py
+++ b/haystack/components/generators/chat/openai.py
@@ -286,7 +286,7 @@ def run(
         *,
         tools: Optional[ToolsType] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invokes chat completion based on the provided messages and generation parameters.
 
@@ -310,6 +310,9 @@ def run(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         if len(messages) == 0:
             return {"replies": []}
 
@@ -358,7 +361,7 @@ async def run_async(
         *,
         tools: Optional[ToolsType] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes chat completion based on the provided messages and generation parameters.
 
@@ -385,6 +388,9 @@ async def run_async(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         # validate and select the streaming callback
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True
diff --git a/haystack/components/generators/chat/openai_responses.py b/haystack/components/generators/chat/openai_responses.py
index 33b6cb9c6c..91b11bb04a 100644
--- a/haystack/components/generators/chat/openai_responses.py
+++ b/haystack/components/generators/chat/openai_responses.py
@@ -297,7 +297,7 @@ def run(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[Union[ToolsType, list[dict]]] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Invokes response generation based on the provided messages and generation parameters.
 
@@ -326,6 +326,9 @@ def run(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         if len(messages) == 0:
             return {"replies": []}
 
@@ -364,7 +367,7 @@ async def run_async(
         generation_kwargs: Optional[dict[str, Any]] = None,
         tools: Optional[Union[ToolsType, list[dict]]] = None,
         tools_strict: Optional[bool] = None,
-    ):
+    ) -> dict[str, list[ChatMessage]]:
         """
         Asynchronously invokes response generation based on the provided messages and generation parameters.
 
@@ -395,6 +398,9 @@ async def run_async(
             A dictionary with the following key:
             - `replies`: A list containing the generated responses as ChatMessage instances.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         # validate and select the streaming callback
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True
diff --git a/haystack/components/generators/hugging_face_local.py b/haystack/components/generators/hugging_face_local.py
index 2a4bb73364..ad747f0bac 100644
--- a/haystack/components/generators/hugging_face_local.py
+++ b/haystack/components/generators/hugging_face_local.py
@@ -229,9 +229,7 @@ def run(
             - replies: A list of strings representing the generated replies.
         """
         if not self._warmed_up:
-            raise RuntimeError(
-                "The component HuggingFaceLocalGenerator was not warmed up. Please call warm_up() before running."
-            )
+            self.warm_up()
 
         # at this point, we know that the pipeline has been initialized
         assert self.pipeline is not None
diff --git a/haystack/components/generators/openai.py b/haystack/components/generators/openai.py
index 4914e19490..48bb0b680d 100644
--- a/haystack/components/generators/openai.py
+++ b/haystack/components/generators/openai.py
@@ -192,7 +192,7 @@ def run(
         system_prompt: Optional[str] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         generation_kwargs: Optional[dict[str, Any]] = None,
-    ):
+    ) -> dict[str, Union[list[str], list[dict[str, Any]]]]:
         """
         Invoke the text generation inference based on the provided messages and generation parameters.
 
@@ -265,4 +265,7 @@ def run(
         for response in completions:
             _check_finish_reason(response.meta)
 
-        return {"replies": [message.text for message in completions], "meta": [message.meta for message in completions]}
+        return {
+            "replies": [message.text or "" for message in completions],
+            "meta": [message.meta for message in completions],
+        }
diff --git a/haystack/components/generators/openai_dalle.py b/haystack/components/generators/openai_dalle.py
index 7a907e2ecc..e5a640312b 100644
--- a/haystack/components/generators/openai_dalle.py
+++ b/haystack/components/generators/openai_dalle.py
@@ -117,14 +117,12 @@ def run(
             to the prompt made by OpenAI.
         """
         if self.client is None:
-            raise RuntimeError(
-                "The component DALLEImageGenerator wasn't warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         size = size or self.size
         quality = quality or self.quality
         response_format = response_format or self.response_format
-        response = self.client.images.generate(
+        response = self.client.images.generate(  # type: ignore[union-attr]
             model=self.model, prompt=prompt, size=size, quality=quality, response_format=response_format, n=1
         )
         if response.data is not None:
diff --git a/haystack/components/preprocessors/document_splitter.py b/haystack/components/preprocessors/document_splitter.py
index 1316b9c996..6162e364c9 100644
--- a/haystack/components/preprocessors/document_splitter.py
+++ b/haystack/components/preprocessors/document_splitter.py
@@ -187,9 +187,7 @@ def run(self, documents: list[Document]):
         :raises ValueError: if the content of a document is None.
         """
         if self._use_sentence_splitter and self.sentence_splitter is None:
-            raise RuntimeError(
-                "The component DocumentSplitter wasn't warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             raise TypeError("DocumentSplitter expects a List of Documents as input.")
diff --git a/haystack/components/preprocessors/recursive_splitter.py b/haystack/components/preprocessors/recursive_splitter.py
index c4f56e96c5..ab23d3e329 100644
--- a/haystack/components/preprocessors/recursive_splitter.py
+++ b/haystack/components/preprocessors/recursive_splitter.py
@@ -461,14 +461,10 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]]:
         :returns:
             A dictionary containing a key "documents" with a List of Documents with smaller chunks of text corresponding
             to the input documents.
-
-        :raises RuntimeError: If the component wasn't warmed up but requires it for sentence splitting or tokenization.
         """
         if not self._is_warmed_up and ("sentence" in self.separators or self.split_units == "token"):
-            raise RuntimeError(
-                "The component RecursiveDocumentSplitter wasn't warmed up but requires it "
-                "for sentence splitting or tokenization. Call 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
+
         docs = []
         for doc in documents:
             if not doc.content or doc.content == "":
diff --git a/haystack/components/rankers/sentence_transformers_diversity.py b/haystack/components/rankers/sentence_transformers_diversity.py
index ee77469f00..64385d4154 100644
--- a/haystack/components/rankers/sentence_transformers_diversity.py
+++ b/haystack/components/rankers/sentence_transformers_diversity.py
@@ -406,14 +406,9 @@ def run(
             - `documents`: List of Document objects that have been selected based on the diversity ranking.
 
         :raises ValueError: If the top_k value is less than or equal to 0.
-        :raises RuntimeError: If the component has not been warmed up.
         """
         if self.model is None:
-            error_msg = (
-                "The component SentenceTransformersDiversityRanker wasn't warmed up. "
-                "Run 'warm_up()' before calling 'run()'."
-            )
-            raise RuntimeError(error_msg)
+            self.warm_up()
 
         if not documents:
             return {"documents": []}
diff --git a/haystack/components/rankers/sentence_transformers_similarity.py b/haystack/components/rankers/sentence_transformers_similarity.py
index c0657f469c..e503f46f47 100644
--- a/haystack/components/rankers/sentence_transformers_similarity.py
+++ b/haystack/components/rankers/sentence_transformers_similarity.py
@@ -233,14 +233,9 @@ def run(
 
         :raises ValueError:
             If `top_k` is not > 0.
-        :raises RuntimeError:
-            If the model is not loaded because `warm_up()` was not called before.
         """
         if self._cross_encoder is None:
-            raise RuntimeError(
-                "The component SentenceTransformersSimilarityRanker wasn't warmed up. "
-                "Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not documents:
             return {"documents": []}
@@ -264,7 +259,8 @@ def run(
 
         activation_fn = Sigmoid() if scale_score else Identity()
 
-        ranking_result = self._cross_encoder.rank(
+        # mypy doesn't know this is set in warm_up
+        ranking_result = self._cross_encoder.rank(  # type: ignore[attr-defined]
             query=prepared_query,
             documents=prepared_documents,
             batch_size=self.batch_size,
diff --git a/haystack/components/rankers/transformers_similarity.py b/haystack/components/rankers/transformers_similarity.py
index 77958b4115..89a8a26d14 100644
--- a/haystack/components/rankers/transformers_similarity.py
+++ b/haystack/components/rankers/transformers_similarity.py
@@ -123,7 +123,7 @@ def __init__(  # noqa: PLR0913, pylint: disable=too-many-positional-arguments
         self.query_prefix = query_prefix
         self.document_prefix = document_prefix
         self.tokenizer = None
-        self.device = None
+        self.device: Optional[ComponentDevice] = None
         self.top_k = top_k
         self.token = token
         self.meta_fields_to_embed = meta_fields_to_embed or []
@@ -165,8 +165,10 @@ def warm_up(self):
                 token=self.token.resolve_value() if self.token else None,
                 **self.tokenizer_kwargs,
             )
-            assert self.model is not None
-            self.device = ComponentDevice.from_multiple(device_map=DeviceMap.from_hf(self.model.hf_device_map))
+            # mypy doesn't know this is set right above
+            self.device = ComponentDevice.from_multiple(
+                device_map=DeviceMap.from_hf(self.model.hf_device_map)  # type: ignore[attr-defined]
+            )
 
     def to_dict(self) -> dict[str, Any]:
         """
@@ -249,14 +251,10 @@ def run(  # pylint: disable=too-many-positional-arguments
         :raises ValueError:
             If `top_k` is not > 0.
             If `scale_score` is True and `calibration_factor` is not provided.
-        :raises RuntimeError:
-            If the model is not loaded because `warm_up()` was not called before.
         """
         # If a model path is provided but the model isn't loaded
         if self.model is None:
-            raise RuntimeError(
-                "The component TransformersSimilarityRanker wasn't warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not documents:
             return {"documents": []}
@@ -292,30 +290,33 @@ def __len__(self):
             def __getitem__(self, item):
                 return {key: self.batch_encoding.data[key][item] for key in self.batch_encoding.data.keys()}
 
-        batch_enc = self.tokenizer(query_doc_pairs, padding=True, truncation=True, return_tensors="pt").to(
-            self.device.first_device.to_torch()
+        # mypy doesn't know this is set in warm_up
+        batch_enc = self.tokenizer(  # type: ignore[misc]
+            query_doc_pairs, padding=True, truncation=True, return_tensors="pt"
+        ).to(
+            self.device.first_device.to_torch()  # type: ignore[union-attr]
         )
         dataset = _Dataset(batch_enc)
         inp_dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False)
 
-        similarity_scores = []
+        list_similarity_scores = []
         with torch.inference_mode():
             for features in inp_dataloader:
-                model_preds = self.model(**features).logits.squeeze(dim=1)
-                similarity_scores.extend(model_preds)
-        similarity_scores = torch.stack(similarity_scores)
+                model_preds = self.model(**features).logits.squeeze(dim=1)  # type: ignore[misc]
+                list_similarity_scores.extend(model_preds)
+        similarity_scores = torch.stack(list_similarity_scores)
 
-        if scale_score:
+        if scale_score and calibration_factor is not None:
             similarity_scores = torch.sigmoid(similarity_scores * calibration_factor)
 
         _, sorted_indices = torch.sort(similarity_scores, descending=True)
 
-        sorted_indices = sorted_indices.cpu().tolist()
-        similarity_scores = similarity_scores.cpu().tolist()
+        list_sorted_indices = sorted_indices.cpu().tolist()
+        list_similarity_scores = similarity_scores.cpu().tolist()
         ranked_docs = []
-        for sorted_index in sorted_indices:
+        for sorted_index in list_sorted_indices:
             i = sorted_index
-            documents[i].score = similarity_scores[i]
+            documents[i].score = list_similarity_scores[i]
             ranked_docs.append(documents[i])
 
         if score_threshold is not None:
diff --git a/haystack/components/readers/extractive.py b/haystack/components/readers/extractive.py
index 74f7d5594f..42e518bfec 100644
--- a/haystack/components/readers/extractive.py
+++ b/haystack/components/readers/extractive.py
@@ -225,7 +225,8 @@ def _preprocess(  # pylint: disable=too-many-positional-arguments
             document_ids.append(i)
             document_contents.append(doc.content)
 
-        encodings_pt = self.tokenizer(  # type: ignore
+        # mypy doesn't know this is set in warm_up
+        encodings_pt = self.tokenizer(  # type: ignore[misc]
             queries,
             document_contents,
             padding=True,
@@ -236,12 +237,9 @@ def _preprocess(  # pylint: disable=too-many-positional-arguments
             stride=stride,
         )
 
-        # To make mypy happy even though self.device is set in warm_up()
-        assert self.device is not None
-        assert self.device.first_device is not None
-
         # Take the first device used by `accelerate`. Needed to pass inputs from the tokenizer to the correct device.
-        first_device = self.device.first_device.to_torch()
+        # mypy doesn't know this is set in warm_up
+        first_device = self.device.first_device.to_torch()  # type: ignore[union-attr]
 
         input_ids = encodings_pt.input_ids.to(first_device)
         attention_mask = encodings_pt.attention_mask.to(first_device)
@@ -575,14 +573,9 @@ def run(  # pylint: disable=too-many-positional-arguments
             If None is provided then all answers are kept.
         :returns:
             List of answers sorted by (desc.) answer score.
-
-        :raises RuntimeError:
-            If the component was not warmed up by calling 'warm_up()' before.
         """
         if self.model is None:
-            raise RuntimeError(
-                "The component ExtractiveReader was not warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not documents:
             return {"answers": []}
@@ -622,7 +615,8 @@ def run(  # pylint: disable=too-many-positional-arguments
             cur_attention_mask = attention_mask[start_index:end_index]
 
             with torch.inference_mode():
-                output = self.model(input_ids=cur_input_ids, attention_mask=cur_attention_mask)
+                # mypy doesn't know this is set in warm_up
+                output = self.model(input_ids=cur_input_ids, attention_mask=cur_attention_mask)  # type: ignore[misc]
             cur_start_logits = output.start_logits
             cur_end_logits = output.end_logits
             if num_batches != 1:
diff --git a/haystack/components/routers/transformers_text_router.py b/haystack/components/routers/transformers_text_router.py
index 5bcd6bc067..af651c23f5 100644
--- a/haystack/components/routers/transformers_text_router.py
+++ b/haystack/components/routers/transformers_text_router.py
@@ -188,17 +188,16 @@ def run(self, text: str) -> dict[str, str]:
 
         :raises TypeError:
             If the input is not a str.
-        :raises RuntimeError:
-            If the pipeline has not been loaded because warm_up() was not called before.
         """
         if self.pipeline is None:
-            raise RuntimeError(
-                "The component TextTransformersRouter wasn't warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not isinstance(text, str):
             raise TypeError("TransformersTextRouter expects a str as input.")
 
-        prediction = self.pipeline([text], return_all_scores=False, function_to_apply="none")
+        # mypy doesn't know this is set in warm_up
+        prediction = self.pipeline(  # type: ignore[misc]
+            [text], return_all_scores=False, function_to_apply="none"
+        )
         label = prediction[0]["label"]
         return {label: text}
diff --git a/haystack/components/routers/zero_shot_text_router.py b/haystack/components/routers/zero_shot_text_router.py
index 0baaa913d6..3f1cc6dca0 100644
--- a/haystack/components/routers/zero_shot_text_router.py
+++ b/haystack/components/routers/zero_shot_text_router.py
@@ -199,18 +199,17 @@ def run(self, text: str) -> dict[str, str]:
 
         :raises TypeError:
             If the input is not a str.
-        :raises RuntimeError:
-            If the pipeline has not been loaded because warm_up() was not called before.
         """
         if self.pipeline is None:
-            raise RuntimeError(
-                "The component TransformersZeroShotTextRouter wasn't warmed up. Run 'warm_up()' before calling 'run()'."
-            )
+            self.warm_up()
 
         if not isinstance(text, str):
             raise TypeError("TransformersZeroShotTextRouter expects a str as input.")
 
-        prediction = self.pipeline([text], candidate_labels=self.labels, multi_label=self.multi_label)
+        # mypy doesn't know this is set in warm_up
+        prediction = self.pipeline(  # type: ignore[misc]
+            [text], candidate_labels=self.labels, multi_label=self.multi_label
+        )
         predicted_scores = prediction[0]["scores"]
         max_score_index = max(range(len(predicted_scores)), key=predicted_scores.__getitem__)
         label = prediction[0]["labels"][max_score_index]
diff --git a/haystack/components/tools/tool_invoker.py b/haystack/components/tools/tool_invoker.py
index 5c49d1371a..98213f6bc6 100644
--- a/haystack/components/tools/tool_invoker.py
+++ b/haystack/components/tools/tool_invoker.py
@@ -540,6 +540,9 @@ def run(
         :raises ToolOutputMergeError:
             If merging tool outputs into state fails and `raise_on_failure` is True.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
+
         tools_with_names = self._tools_with_names
         if tools is not None:
             tools_with_names = self._validate_and_prepare_tools(tools)
@@ -672,6 +675,8 @@ async def run_async(
         :raises ToolOutputMergeError:
             If merging tool outputs into state fails and `raise_on_failure` is True.
         """
+        if not self._is_warmed_up:
+            self.warm_up()
 
         tools_with_names = self._tools_with_names
         if tools is not None:
diff --git a/releasenotes/notes/refactor-warm-up-components-c2777fef28a70b61.yaml b/releasenotes/notes/refactor-warm-up-components-c2777fef28a70b61.yaml
new file mode 100644
index 0000000000..cae8b434f9
--- /dev/null
+++ b/releasenotes/notes/refactor-warm-up-components-c2777fef28a70b61.yaml
@@ -0,0 +1,4 @@
+---
+features:
+  - |
+    Components that define ``warm_up`` now auto run it on first use, removing the need for manual calls and preventing errors in standalone usage.
diff --git a/test/components/embedders/image/test_sentence_transformers_doc_image_embedder.py b/test/components/embedders/image/test_sentence_transformers_doc_image_embedder.py
index 2e6b1525b3..4d8c6bb274 100644
--- a/test/components/embedders/image/test_sentence_transformers_doc_image_embedder.py
+++ b/test/components/embedders/image/test_sentence_transformers_doc_image_embedder.py
@@ -220,12 +220,6 @@ def test_run(self, test_files_path):
             assert new_doc.meta["embedding_source"]["type"] == "image"
             assert "file_path_meta_field" in new_doc.meta["embedding_source"]
 
-    def test_run_no_warmup(self):
-        embedder = SentenceTransformersDocumentImageEmbedder(model="model")
-
-        with pytest.raises(RuntimeError, match="The embedding model has not been loaded."):
-            embedder.run(documents=[Document(content="test")])
-
     def test_run_wrong_input_format(self):
         embedder = SentenceTransformersDocumentImageEmbedder(model="model")
 
@@ -329,7 +323,6 @@ def test_live_run(self, test_files_path, monkeypatch):
         monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
 
         embedder = SentenceTransformersDocumentImageEmbedder(model="sentence-transformers/clip-ViT-B-32")
-        embedder.warm_up()
 
         documents = [
             Document(
diff --git a/test/components/embedders/test_sentence_transformers_sparse_document_embedder.py b/test/components/embedders/test_sentence_transformers_sparse_document_embedder.py
index 1574ffc134..4e05823f54 100644
--- a/test/components/embedders/test_sentence_transformers_sparse_document_embedder.py
+++ b/test/components/embedders/test_sentence_transformers_sparse_document_embedder.py
@@ -286,11 +286,6 @@ def test_run_wrong_input_format(self):
         ):
             embedder.run(documents=list_integers_input)
 
-    def test_run_no_warmup(self):
-        embedder = SentenceTransformersSparseDocumentEmbedder(model="model")
-        with pytest.raises(RuntimeError, match="The embedding model has not been loaded."):
-            embedder.run(documents=[Document(content="test")])
-
     def test_embed_metadata(self):
         embedder = SentenceTransformersSparseDocumentEmbedder(
             model="model", meta_fields_to_embed=["meta_field"], embedding_separator="\n"
@@ -435,7 +430,6 @@ def test_live_run_sparse_document_embedder(self, monkeypatch):
             embedding_separator=" | ",
             device=ComponentDevice.from_str("cpu"),
         )
-        embedder.warm_up()
         result = embedder.run(documents=docs)
         documents_with_embeddings = result["documents"]
 
diff --git a/test/components/embedders/test_sentence_transformers_sparse_text_embedder.py b/test/components/embedders/test_sentence_transformers_sparse_text_embedder.py
index 4e018d01e5..c8a36f75c1 100644
--- a/test/components/embedders/test_sentence_transformers_sparse_text_embedder.py
+++ b/test/components/embedders/test_sentence_transformers_sparse_text_embedder.py
@@ -210,11 +210,6 @@ def test_warmup_doesnt_reload(self, mocked_factory):
         embedder.warm_up()
         mocked_factory.get_embedding_backend.assert_called_once()
 
-    def test_run_no_warmup(self):
-        embedder = SentenceTransformersSparseTextEmbedder(model="model")
-        with pytest.raises(RuntimeError, match="The embedding model has not been loaded"):
-            embedder.run(text="a nice text to embed")
-
     def test_run(self):
         embedder = SentenceTransformersSparseTextEmbedder(model="model")
         embedder.embedding_backend = MagicMock()
@@ -337,7 +332,6 @@ def test_live_run_sparse_text_embedder(self, monkeypatch):
         embedder = SentenceTransformersSparseTextEmbedder(
             model="sparse-encoder-testing/splade-bert-tiny-nq", device=ComponentDevice.from_str("cpu")
         )
-        embedder.warm_up()
         result = embedder.run(text=text)
         sparse_embedding = result["sparse_embedding"]
 
diff --git a/test/components/evaluators/test_sas_evaluator.py b/test/components/evaluators/test_sas_evaluator.py
index 6a345ec33e..bbb8210991 100644
--- a/test/components/evaluators/test_sas_evaluator.py
+++ b/test/components/evaluators/test_sas_evaluator.py
@@ -89,21 +89,6 @@ def test_run_with_none_in_predictions(self):
         with pytest.raises(ValueError):
             evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
 
-    def test_run_not_warmed_up(self):
-        evaluator = SASEvaluator()
-        ground_truths = [
-            "A construction budget of US $2.3 billion",
-            "The Eiffel Tower, completed in 1889, symbolizes Paris's cultural magnificence.",
-            "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
-        ]
-        predictions = [
-            "A construction budget of US $2.3 billion",
-            "The Eiffel Tower, completed in 1889, symbolizes Paris's cultural magnificence.",
-            "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
-        ]
-        with pytest.raises(RuntimeError):
-            evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
-
     @pytest.mark.integration
     @pytest.mark.slow
     def test_run_with_matching_predictions(self, monkeypatch):
@@ -119,7 +104,6 @@ def test_run_with_matching_predictions(self, monkeypatch):
             "The Eiffel Tower, completed in 1889, symbolizes Paris's cultural magnificence.",
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
-        evaluator.warm_up()
         result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
 
         assert len(result) == 2
@@ -135,7 +119,6 @@ def test_run_with_single_prediction(self, monkeypatch):
         )
 
         ground_truths = ["US $2.3 billion"]
-        evaluator.warm_up()
         result = evaluator.run(
             ground_truth_answers=ground_truths, predicted_answers=["A construction budget of US $2.3 billion"]
         )
@@ -158,7 +141,6 @@ def test_run_with_mismatched_predictions(self, monkeypatch):
             "The Eiffel Tower, completed in 1889, symbolizes Paris's cultural magnificence.",
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
-        evaluator.warm_up()
         result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
         assert len(result) == 2
         assert result["score"] == pytest.approx(0.912335)
@@ -179,7 +161,6 @@ def test_run_with_cross_encoder_model(self, monkeypatch):
             "The Eiffel Tower, completed in 1889, symbolizes Paris's cultural magnificence.",
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
-        evaluator.warm_up()
         result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
         assert len(result) == 2
         assert result["score"] == pytest.approx(0.938108, abs=1e-5)
diff --git a/test/components/extractors/test_named_entity_extractor.py b/test/components/extractors/test_named_entity_extractor.py
index 06322f1eaf..ad8b444137 100644
--- a/test/components/extractors/test_named_entity_extractor.py
+++ b/test/components/extractors/test_named_entity_extractor.py
@@ -166,13 +166,3 @@ def test_named_entity_extractor_run():
         assert "named_entities" in result["documents"][0].meta
         assert result["documents"][0].meta["named_entities"] == expected_annotations[0]
         assert "named_entities" not in documents[0].meta
-
-
-def test_named_entity_extractor_run_not_warmed_up():
-    """Test that run method raises error when not warmed up."""
-    extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.HUGGING_FACE, model="dslim/bert-base-NER")
-
-    documents = [Document(content="Test document")]
-
-    with pytest.raises(RuntimeError, match="The component NamedEntityExtractor was not warmed up"):
-        extractor.run(documents=documents)
diff --git a/test/components/generators/chat/test_hugging_face_local.py b/test/components/generators/chat/test_hugging_face_local.py
index 78f24073a4..650af4097a 100644
--- a/test/components/generators/chat/test_hugging_face_local.py
+++ b/test/components/generators/chat/test_hugging_face_local.py
@@ -493,7 +493,6 @@ def test_live_run(self, monkeypatch):
         llm = HuggingFaceLocalChatGenerator(
             model="Qwen/Qwen2.5-0.5B-Instruct", generation_kwargs={"max_new_tokens": 50}
         )
-        llm.warm_up()
 
         result = llm.run(messages)
 
@@ -679,10 +678,6 @@ async def test_async_error_handling(self, model_info_mock, mock_pipeline_with_to
         """Test error handling in async context"""
         generator = HuggingFaceLocalChatGenerator(model="mocked-model")
 
-        # Test without warm_up
-        with pytest.raises(RuntimeError, match="The generation model has not been loaded"):
-            await generator.run_async(messages=[ChatMessage.from_user("test")])
-
         # Test with invalid streaming callback
         generator.pipeline = mock_pipeline_with_tokenizer
         with pytest.raises(ValueError, match="Using tools and streaming at the same time is not supported"):
@@ -809,7 +804,6 @@ async def streaming_callback(chunk: StreamingChunk) -> None:
             generation_kwargs={"max_new_tokens": 50},
             streaming_callback=streaming_callback,
         )
-        llm.warm_up()
 
         response = await llm.run_async(
             messages=[ChatMessage.from_user("Please create a summary about the following topic: Capital of France")]
diff --git a/test/components/generators/test_hugging_face_local_generator.py b/test/components/generators/test_hugging_face_local_generator.py
index c6985ab22d..1c3821264e 100644
--- a/test/components/generators/test_hugging_face_local_generator.py
+++ b/test/components/generators/test_hugging_face_local_generator.py
@@ -383,14 +383,6 @@ def streaming_callback_handler(x):
         # the tokenizer should be set, here it is a mock
         assert streamer.tokenizer
 
-    def test_run_fails_without_warm_up(self):
-        generator = HuggingFaceLocalGenerator(
-            model="google/flan-t5-base", task="text2text-generation", generation_kwargs={"max_new_tokens": 100}
-        )
-
-        with pytest.raises(RuntimeError, match="The component HuggingFaceLocalGenerator was not warmed up"):
-            generator.run(prompt="irrelevant")
-
     def test_stop_words_criteria_with_a_mocked_tokenizer(self):
         """
         Test that StopWordsCriteria will caught stop word tokens in a continuous and sequential order in the input_ids
@@ -471,7 +463,6 @@ def test_hf_pipeline_runs_with_our_criteria(self, monkeypatch):
     def test_live_run(self, monkeypatch):
         monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         llm = HuggingFaceLocalGenerator(model="Qwen/Qwen2.5-0.5B-Instruct", generation_kwargs={"max_new_tokens": 50})
-        llm.warm_up()
 
         # You must use the `apply_chat_template` method to add the generation prompt to properly include the instruction
         # tokens in the prompt. Otherwise, the model will not generate the expected output.
diff --git a/test/components/preprocessors/test_recursive_splitter.py b/test/components/preprocessors/test_recursive_splitter.py
index 2858c69562..d91ac883bd 100644
--- a/test/components/preprocessors/test_recursive_splitter.py
+++ b/test/components/preprocessors/test_recursive_splitter.py
@@ -120,7 +120,6 @@ def test_run_using_custom_sentence_tokenizer():
         separators=["\n\n", "\n", "sentence", " "],
         sentence_splitter_params={"language": "en", "use_split_rules": True, "keep_white_spaces": False},
     )
-    splitter.warm_up()
     text = """Artificial intelligence (AI) - Introduction
 
 AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems.
@@ -317,7 +316,6 @@ def test_run_split_by_sentence_count_page_breaks_split_unit_char() -> None:
     document_splitter = RecursiveDocumentSplitter(
         separators=["sentence"], split_length=28, split_overlap=0, split_unit="char"
     )
-    document_splitter.warm_up()
 
     text = (
         "Sentence on page 1. Another on page 1.\fSentence on page 2. Another on page 2.\f"
@@ -462,7 +460,6 @@ def test_run_custom_sentence_tokenizer_document_and_overlap_char_unit():
     splitter = RecursiveDocumentSplitter(split_length=25, split_overlap=10, separators=["sentence"], split_unit="char")
     text = "This is sentence one. This is sentence two. This is sentence three."
 
-    splitter.warm_up()
     doc = Document(content=text)
     doc_chunks = splitter.run([doc])["documents"]
 
@@ -668,7 +665,6 @@ def test_run_split_by_sentence_count_page_breaks_word_unit() -> None:
     document_splitter = RecursiveDocumentSplitter(
         separators=["sentence"], split_length=7, split_overlap=0, split_unit="word"
     )
-    document_splitter.warm_up()
 
     text = (
         "Sentence on page 1. Another on page 1.\fSentence on page 2. Another on page 2.\f"
@@ -847,7 +843,6 @@ def test_run_serialization_in_pipeline():
 @pytest.mark.integration
 def test_run_split_by_token_count():
     splitter = RecursiveDocumentSplitter(split_length=5, separators=["."], split_unit="token")
-    splitter.warm_up()
 
     text = "This is a test. This is another test. This is the final test."
     doc = Document(content=text)
@@ -863,7 +858,6 @@ def test_run_split_by_token_count():
 @pytest.mark.integration
 def test_run_split_by_token_count_with_html_tags():
     splitter = RecursiveDocumentSplitter(split_length=4, separators=["."], split_unit="token")
-    splitter.warm_up()
 
     text = "This is a test. <h><a><y><s><t><a><c><k><c><o><r><e> This is the final test."
     doc = Document(content=text)
@@ -875,7 +869,6 @@ def test_run_split_by_token_count_with_html_tags():
 @pytest.mark.integration
 def test_run_split_by_token_with_sentence_tokenizer():
     splitter = RecursiveDocumentSplitter(split_length=4, separators=["sentence"], split_unit="token")
-    splitter.warm_up()
 
     text = "This is sentence one. This is sentence two."
     doc = Document(content=text)
@@ -891,7 +884,6 @@ def test_run_split_by_token_with_sentence_tokenizer():
 @pytest.mark.integration
 def test_run_split_by_token_with_empty_document(caplog: LogCaptureFixture):
     splitter = RecursiveDocumentSplitter(split_length=4, separators=["."], split_unit="token")
-    splitter.warm_up()
 
     empty_doc = Document(content="")
     doc_chunks = splitter.run([empty_doc])
@@ -904,7 +896,6 @@ def test_run_split_by_token_with_empty_document(caplog: LogCaptureFixture):
 @pytest.mark.integration
 def test_run_split_by_token_with_fallback():
     splitter = RecursiveDocumentSplitter(split_length=2, separators=["."], split_unit="token")
-    splitter.warm_up()
 
     text = "This is a very long sentence that will need to be split into smaller chunks."
     doc = Document(content=text)
@@ -918,7 +909,6 @@ def test_run_split_by_token_with_fallback():
 @pytest.mark.integration
 def test_run_split_by_token_with_overlap_and_fallback():
     splitter = RecursiveDocumentSplitter(split_length=4, split_overlap=2, separators=["."], split_unit="token")
-    splitter.warm_up()
 
     text = "This is a test. This is another test. This is the final test."
     doc = Document(content=text)
@@ -935,24 +925,6 @@ def test_run_split_by_token_with_overlap_and_fallback():
     assert chunks[7].content == " final test."
 
 
-def test_run_without_warm_up_raises_error():
-    docs = [Document(content="text")]
-
-    splitter_token = RecursiveDocumentSplitter(split_unit="token")
-    with pytest.raises(RuntimeError, match="warmed up"):
-        splitter_token.run(docs)
-
-    splitter_sentence = RecursiveDocumentSplitter(separators=["sentence"])
-    with pytest.raises(RuntimeError, match="warmed up"):
-        splitter_sentence.run(docs)
-
-    # Test that no error is raised when warm_up is not required
-    splitter_no_warmup = RecursiveDocumentSplitter(separators=["."])
-    result = splitter_no_warmup.run(docs)
-    assert len(result["documents"]) == 1
-    assert result["documents"][0].content == "text"
-
-
 def test_run_complex_text_with_multiple_separators():
     """
     Test that RecursiveDocumentSplitter correctly handles complex text with multiple separators and chunks that exceed
@@ -975,7 +947,6 @@ def test_run_complex_text_with_multiple_separators():
     splitter = RecursiveDocumentSplitter(
         split_length=200, split_overlap=0, split_unit="char", separators=["\n\n", "\n", " "]
     )
-    splitter.warm_up()
     result = splitter.run([doc])
     chunks = result["documents"]
 
@@ -1000,7 +971,6 @@ def test_recursive_splitter_generates_unique_ids_and_correct_meta():
     source_doc = Document(content=text)
 
     splitter = RecursiveDocumentSplitter(split_length=3)
-    splitter.warm_up()
 
     chunks = splitter.run([source_doc])["documents"]
 
diff --git a/test/components/rankers/test_sentence_transformers_diversity.py b/test/components/rankers/test_sentence_transformers_diversity.py
index 69ca3d536e..6bef271984 100644
--- a/test/components/rankers/test_sentence_transformers_diversity.py
+++ b/test/components/rankers/test_sentence_transformers_diversity.py
@@ -254,20 +254,6 @@ def test_run_invalid_strategy(self):
                 model="sentence-transformers/all-MiniLM-L6-v2", similarity="cosine", strategy=strategy
             )
 
-    @pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
-    def test_run_without_warm_up(self, similarity):
-        """
-        Tests that run method raises ComponentError if model is not warmed up
-        """
-        ranker = SentenceTransformersDiversityRanker(
-            model="sentence-transformers/all-MiniLM-L6-v2", top_k=1, similarity=similarity
-        )
-        documents = [Document(content="doc1"), Document(content="doc2")]
-
-        error_msg = "The component SentenceTransformersDiversityRanker wasn't warmed up."
-        with pytest.raises(RuntimeError, match=error_msg):
-            ranker.run(query="test query", documents=documents)
-
     @pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
     def test_warm_up(self, similarity, monkeypatch):
         """
@@ -585,7 +571,6 @@ def test_run_real_world_use_case(self, similarity, monkeypatch):
         ranker = SentenceTransformersDiversityRanker(
             model="sentence-transformers-testing/stsb-bert-tiny-safetensors", similarity=similarity
         )
-        ranker.warm_up()
         query = "What are the reasons for long-standing animosities between Russia and Poland?"
 
         doc1 = Document(
@@ -674,7 +659,6 @@ def test_run_with_maximum_margin_relevance_strategy(self, similarity, monkeypatc
             similarity=similarity,
             strategy="maximum_margin_relevance",
         )
-        ranker.warm_up()
 
         # lambda_threshold=1, the most relevant document should be returned first
         results = ranker.run(query=query, documents=docs, lambda_threshold=1, top_k=len(docs))
diff --git a/test/components/rankers/test_sentence_transformers_similarity.py b/test/components/rankers/test_sentence_transformers_similarity.py
index f0622c630a..3a4bed1625 100644
--- a/test/components/rankers/test_sentence_transformers_similarity.py
+++ b/test/components/rankers/test_sentence_transformers_similarity.py
@@ -270,11 +270,6 @@ def test_returns_empty_list_if_no_documents_are_provided(self):
         output = ranker.run(query="City in Germany", documents=[])
         assert not output["documents"]
 
-    def test_raises_component_error_if_model_not_warmed_up(self):
-        ranker = SentenceTransformersSimilarityRanker()
-        with pytest.raises(RuntimeError):
-            ranker.run(query="query", documents=[Document(content="document")])
-
     def test_embed_meta(self):
         ranker = SentenceTransformersSimilarityRanker(
             model="model", meta_fields_to_embed=["meta_field"], embedding_separator="\n"
@@ -373,7 +368,6 @@ def test_scores_cast_to_python_float_when_numpy_scalars_returned(self):
     @pytest.mark.slow
     def test_run(self):
         ranker = SentenceTransformersSimilarityRanker(model="cross-encoder-testing/reranker-bert-tiny-gooaq-bce")
-        ranker.warm_up()
 
         query = "City in Bosnia and Herzegovina"
         docs_before_texts = ["Berlin", "Belgrade", "Sarajevo"]
@@ -401,7 +395,6 @@ def test_run_top_k(self):
         ranker = SentenceTransformersSimilarityRanker(
             model="cross-encoder-testing/reranker-bert-tiny-gooaq-bce", top_k=2
         )
-        ranker.warm_up()
 
         query = "City in Bosnia and Herzegovina"
         docs_before_texts = ["Berlin", "Belgrade", "Sarajevo"]
@@ -426,7 +419,6 @@ def test_run_single_document(self):
         ranker = SentenceTransformersSimilarityRanker(
             model="cross-encoder-testing/reranker-bert-tiny-gooaq-bce", device=None
         )
-        ranker.warm_up()
         docs_before = [Document(content="Berlin")]
         output = ranker.run(query="City in Germany", documents=docs_before)
         docs_after = output["documents"]
diff --git a/test/components/rankers/test_transformers_similarity.py b/test/components/rankers/test_transformers_similarity.py
index d85f7c75e7..a4e0c8a4fa 100644
--- a/test/components/rankers/test_transformers_similarity.py
+++ b/test/components/rankers/test_transformers_similarity.py
@@ -338,17 +338,14 @@ def __init__(self):
 
     def test_returns_empty_list_if_no_documents_are_provided(self):
         sampler = TransformersSimilarityRanker()
+        # Mock all attributes that are set during warm_up
         sampler.model = MagicMock()
+        sampler.tokenizer = MagicMock()
+        sampler.device = MagicMock()
 
         output = sampler.run(query="City in Germany", documents=[])
         assert not output["documents"]
 
-    #  Raises ComponentError if model is not warmed up
-    def test_raises_component_error_if_model_not_warmed_up(self):
-        sampler = TransformersSimilarityRanker()
-        with pytest.raises(RuntimeError):
-            sampler.run(query="query", documents=[Document(content="document")])
-
     @pytest.mark.integration
     @pytest.mark.slow
     def test_run(self):