diff --git a/poetry.lock b/poetry.lock
index 4309eda3..1ef01ee8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -536,13 +536,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
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+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.extras]
@@ -664,61 +664,61 @@ tqdm = ["tqdm"]
 
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.63.0rc1)"]
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 name = "grpcio-tools"
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 [package.dependencies]
@@ -1711,6 +1703,33 @@ packaging = "*"
 protobuf = "*"
 sympy = "*"
 
+[[package]]
+name = "onnxruntime-gpu"
+version = "1.17.1"
+description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
+optional = true
+python-versions = "*"
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+flatbuffers = "*"
+numpy = ">=1.21.6"
+packaging = "*"
+protobuf = "*"
+sympy = "*"
+
 [[package]]
 name = "packaging"
 version = "24.0"
@@ -1797,28 +1816,29 @@ files = [
 
 [[package]]
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 [[package]]
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 python-versions = ">=3.8"
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 [package.extras]
@@ -1891,22 +1911,22 @@ files = [
 
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 name = "protobuf"
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+version = "5.26.1"
 description = ""
 optional = false
 python-versions = ">=3.8"
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+    {file = "pyzmq-26.0.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:6926dd14cfe6967d3322640b6d5c3c3039db71716a5e43cca6e3b474e73e0b36"},
+    {file = "pyzmq-26.0.2.tar.gz", hash = "sha256:f0f9bb370449158359bb72a3e12c658327670c0ffe6fbcd1af083152b64f9df0"},
 ]
 
 [package.dependencies]
@@ -2380,13 +2400,13 @@ resolved_reference = "a90cdd5925783c2b0ed3b8d39897cd4eaf942e2a"
 
 [[package]]
 name = "referencing"
-version = "0.34.0"
+version = "0.35.0"
 description = "JSON Referencing + Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "referencing-0.34.0-py3-none-any.whl", hash = "sha256:d53ae300ceddd3169f1ffa9caf2cb7b769e92657e4fafb23d34b93679116dfd4"},
-    {file = "referencing-0.34.0.tar.gz", hash = "sha256:5773bd84ef41799a5a8ca72dc34590c041eb01bf9aa02632b4a973fb0181a844"},
+    {file = "referencing-0.35.0-py3-none-any.whl", hash = "sha256:8080727b30e364e5783152903672df9b6b091c926a146a759080b62ca3126cd6"},
+    {file = "referencing-0.35.0.tar.gz", hash = "sha256:191e936b0c696d0af17ad7430a3dc68e88bc11be6514f4757dc890f04ab05889"},
 ]
 
 [package.dependencies]
@@ -2741,13 +2761,13 @@ mpmath = ">=0.19"
 
 [[package]]
 name = "tinycss2"
-version = "1.2.1"
+version = "1.3.0"
 description = "A tiny CSS parser"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"},
-    {file = "tinycss2-1.2.1.tar.gz", hash = "sha256:8cff3a8f066c2ec677c06dbc7b45619804a6938478d9d73c284b29d14ecb0627"},
+    {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
+    {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
 ]
 
 [package.dependencies]
@@ -2755,7 +2775,7 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "tokenizers"
@@ -2937,18 +2957,18 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.2"
+version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"},
-    {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"},
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "types-protobuf"
@@ -3042,8 +3062,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [extras]
 fastembed = ["fastembed"]
+fastembed-gpu = ["fastembed", "onnxruntime-gpu"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8"
-content-hash = "d9f322012dac6de2c4f537befddade3c059b8f2dcd9420ff742450739da4dc03"
+content-hash = "1663b3658fbbf2dc2af72af44fce9a1b3d1f038c192275b9e92b65f9c00fd4f0"
diff --git a/pyproject.toml b/pyproject.toml
index 25b7d281..6bf65140 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,6 +29,9 @@ portalocker = "^2.7.0"
 fastembed = [
     { version = "0.2.6", optional = true, python = "<3.13" }
 ]
+onnxruntime-gpu = [
+    { version = "^1.17.0", optional = true, python = "<3.13" }
+]
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.1"
@@ -58,6 +61,7 @@ types-protobuf = "^4.21.0.5"
 
 [tool.poetry.extras]
 fastembed = ["fastembed"]
+fastembed-gpu = ["fastembed", "onnxruntime-gpu"]
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/qdrant_client/async_qdrant_fastembed.py b/qdrant_client/async_qdrant_fastembed.py
index 888d8e8a..d44d958f 100644
--- a/qdrant_client/async_qdrant_fastembed.py
+++ b/qdrant_client/async_qdrant_fastembed.py
@@ -12,7 +12,7 @@
 import uuid
 import warnings
 from itertools import tee
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
 
 from qdrant_client.async_client_base import AsyncQdrantBase
 from qdrant_client.conversions import common_types as types
@@ -21,8 +21,7 @@
 from qdrant_client.hybrid.fusion import reciprocal_rank_fusion
 
 try:
-    from fastembed import TextEmbedding
-    from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
+    from fastembed import SparseTextEmbedding, TextEmbedding
 except ImportError:
     TextEmbedding = None
     SparseTextEmbedding = None
@@ -51,8 +50,7 @@ def __init__(self, **kwargs: Any):
         self._embedding_model_name: Optional[str] = None
         self._sparse_embedding_model_name: Optional[str] = None
         try:
-            from fastembed import TextEmbedding
-            from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
+            from fastembed import SparseTextEmbedding, TextEmbedding
 
             self.__class__._FASTEMBED_INSTALLED = True
         except ImportError:
@@ -75,6 +73,7 @@ def set_model(
         max_length: Optional[int] = None,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> None:
         """
@@ -86,6 +85,9 @@ def set_model(
                                        Can be set using the `FASTEMBED_CACHE_PATH` env variable.
                                        Defaults to `fastembed_cache` in the system's temp directory.
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers: The list of onnx providers (with or without options) to use. Defaults to None.
+                Example configuration:
+                https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#configuration-options
         Raises:
             ValueError: If embedding model is not supported.
             ImportError: If fastembed is not installed.
@@ -100,7 +102,11 @@ def set_model(
                 stacklevel=2,
             )
         self._get_or_init_model(
-            model_name=embedding_model_name, cache_dir=cache_dir, threads=threads, **kwargs
+            model_name=embedding_model_name,
+            cache_dir=cache_dir,
+            threads=threads,
+            providers=providers,
+            **kwargs,
         )
         self._embedding_model_name = embedding_model_name
 
@@ -109,6 +115,7 @@ def set_sparse_model(
         embedding_model_name: Optional[str],
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
     ) -> None:
         """
         Set sparse embedding model to use for hybrid search over documents in combination with dense embeddings.
@@ -119,6 +126,9 @@ def set_sparse_model(
                                        Can be set using the `FASTEMBED_CACHE_PATH` env variable.
                                        Defaults to `fastembed_cache` in the system's temp directory.
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers: The list of onnx providers (with or without options) to use. Defaults to None.
+                Example configuration:
+                https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#configuration-options
         Raises:
             ValueError: If embedding model is not supported.
             ImportError: If fastembed is not installed.
@@ -128,7 +138,10 @@ def set_sparse_model(
         """
         if embedding_model_name is not None:
             self._get_or_init_sparse_model(
-                model_name=embedding_model_name, cache_dir=cache_dir, threads=threads
+                model_name=embedding_model_name,
+                cache_dir=cache_dir,
+                threads=threads,
+                providers=providers,
             )
         self._sparse_embedding_model_name = embedding_model_name
 
@@ -155,6 +168,7 @@ def _get_or_init_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> "TextEmbedding":
         if model_name in cls.embedding_models:
@@ -165,7 +179,11 @@ def _get_or_init_model(
                 f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
             )
         cls.embedding_models[model_name] = TextEmbedding(
-            model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=threads,
+            providers=providers,
+            **kwargs,
         )
         return cls.embedding_models[model_name]
 
@@ -175,6 +193,7 @@ def _get_or_init_sparse_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> "SparseTextEmbedding":
         if model_name in cls.sparse_embedding_models:
@@ -185,7 +204,11 @@ def _get_or_init_sparse_model(
                 f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_SPARSE_EMBEDDING_MODELS}"
             )
         cls.sparse_embedding_models[model_name] = SparseTextEmbedding(
-            model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=threads,
+            providers=providers,
+            **kwargs,
         )
         return cls.sparse_embedding_models[model_name]
 
diff --git a/qdrant_client/qdrant_fastembed.py b/qdrant_client/qdrant_fastembed.py
index 8850f4ac..810397a4 100644
--- a/qdrant_client/qdrant_fastembed.py
+++ b/qdrant_client/qdrant_fastembed.py
@@ -1,7 +1,7 @@
 import uuid
 import warnings
 from itertools import tee
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
 
 from qdrant_client.client_base import QdrantBase
 from qdrant_client.conversions import common_types as types
@@ -10,13 +10,11 @@
 from qdrant_client.hybrid.fusion import reciprocal_rank_fusion
 
 try:
-    from fastembed import TextEmbedding
-    from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
+    from fastembed import SparseTextEmbedding, TextEmbedding
 except ImportError:
     TextEmbedding = None
     SparseTextEmbedding = None
 
-
 SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = (
     {
         model["model"]: (model["dim"], models.Distance.COSINE)
@@ -45,8 +43,7 @@ def __init__(self, **kwargs: Any):
         self._embedding_model_name: Optional[str] = None
         self._sparse_embedding_model_name: Optional[str] = None
         try:
-            from fastembed import TextEmbedding  # noqa: F401
-            from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
+            from fastembed import SparseTextEmbedding, TextEmbedding  # noqa: F401
 
             self.__class__._FASTEMBED_INSTALLED = True
         except ImportError:
@@ -70,6 +67,7 @@ def set_model(
         max_length: Optional[int] = None,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> None:
         """
@@ -81,6 +79,9 @@ def set_model(
                                        Can be set using the `FASTEMBED_CACHE_PATH` env variable.
                                        Defaults to `fastembed_cache` in the system's temp directory.
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers: The list of onnx providers (with or without options) to use. Defaults to None.
+                Example configuration:
+                https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#configuration-options
         Raises:
             ValueError: If embedding model is not supported.
             ImportError: If fastembed is not installed.
@@ -101,6 +102,7 @@ def set_model(
             model_name=embedding_model_name,
             cache_dir=cache_dir,
             threads=threads,
+            providers=providers,
             **kwargs,
         )
         self._embedding_model_name = embedding_model_name
@@ -110,6 +112,7 @@ def set_sparse_model(
         embedding_model_name: Optional[str],
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
     ) -> None:
         """
         Set sparse embedding model to use for hybrid search over documents in combination with dense embeddings.
@@ -120,6 +123,9 @@ def set_sparse_model(
                                        Can be set using the `FASTEMBED_CACHE_PATH` env variable.
                                        Defaults to `fastembed_cache` in the system's temp directory.
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers: The list of onnx providers (with or without options) to use. Defaults to None.
+                Example configuration:
+                https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#configuration-options
         Raises:
             ValueError: If embedding model is not supported.
             ImportError: If fastembed is not installed.
@@ -132,6 +138,7 @@ def set_sparse_model(
                 model_name=embedding_model_name,
                 cache_dir=cache_dir,
                 threads=threads,
+                providers=providers,
             )
         self._sparse_embedding_model_name = embedding_model_name
 
@@ -163,6 +170,7 @@ def _get_or_init_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> "TextEmbedding":
         if model_name in cls.embedding_models:
@@ -179,6 +187,7 @@ def _get_or_init_model(
             model_name=model_name,
             cache_dir=cache_dir,
             threads=threads,
+            providers=providers,
             **kwargs,
         )
         return cls.embedding_models[model_name]
@@ -189,6 +198,7 @@ def _get_or_init_sparse_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
         **kwargs: Any,
     ) -> "SparseTextEmbedding":
         if model_name in cls.sparse_embedding_models:
@@ -205,6 +215,7 @@ def _get_or_init_sparse_model(
             model_name=model_name,
             cache_dir=cache_dir,
             threads=threads,
+            providers=providers,
             **kwargs,
         )
         return cls.sparse_embedding_models[model_name]