diff --git a/README.md b/README.md
index cc75d4a6..5fd00bbb 100644
--- a/README.md
+++ b/README.md
@@ -107,6 +107,23 @@ search_result = client.query(
 print(search_result)
 ```
 
+FastEmbed can also utilise GPU for faster embeddings. To enable GPU support, install
+```bash
+pip install 'qdrant-client[fastembed-gpu]'
+```
+
+```python
+from qdrant_client import QdrantClient
+
+# Initialize the client
+client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")
+client.set_model(client.DEFAULT_EMBEDDING_MODEL, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+```
+
+> Note: `fastembed-gpu` and `fastembed` are mutually exclusive. You can only install one of them.
+>
+> If you previously installed `fastembed`, you might need to start from a fresh environment to install `fastembed-gpu`.
+
 ## Connect to Qdrant server
 
 To connect to Qdrant server, simply specify host and port:
diff --git a/poetry.lock b/poetry.lock
index 4309eda3..3c58a8ff 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -536,13 +536,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
-    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -564,13 +564,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastembed"
-version = "0.2.6"
+version = "0.2.7"
 description = "Fast, light, accurate library built for retrieval embedding generation"
 optional = true
 python-versions = "<3.13,>=3.8.0"
 files = [
-    {file = "fastembed-0.2.6-py3-none-any.whl", hash = "sha256:3e18633291722087abebccccd7fcdffafef643cb22d203370d7fad4fa83c10fb"},
-    {file = "fastembed-0.2.6.tar.gz", hash = "sha256:adaed5b46e19cc1bbe5f98f2b3ffecfc4d2a48d27512e28ff5bfe92a42649a66"},
+    {file = "fastembed-0.2.7-py3-none-any.whl", hash = "sha256:8f21e65e6d5d06bd8727488906c6c4eda8eb86d81be8879a54846dfe8a23c9d3"},
+    {file = "fastembed-0.2.7.tar.gz", hash = "sha256:f5537e3680694afcd3d806c19dd4514030fdc3144e7e9a9db0dfece771922503"},
 ]
 
 [package.dependencies]
@@ -583,7 +583,30 @@ numpy = [
 onnx = ">=1.15.0,<2.0.0"
 onnxruntime = ">=1.17.0,<2.0.0"
 requests = ">=2.31,<3.0"
-tokenizers = ">=0.15.1,<0.16.0"
+tokenizers = ">=0.15,<0.16"
+tqdm = ">=4.66,<5.0"
+
+[[package]]
+name = "fastembed-gpu"
+version = "0.2.7"
+description = "Fast, light, accurate library built for retrieval embedding generation"
+optional = true
+python-versions = "<3.13,>=3.8.0"
+files = [
+    {file = "fastembed_gpu-0.2.7-py3-none-any.whl", hash = "sha256:fdde2c0b78a837c38a09518299efc8c7e9a13d6a7cb21dd3072230b2a30593ed"},
+    {file = "fastembed_gpu-0.2.7.tar.gz", hash = "sha256:1fc33ce59cd6fd2816b348c0b4527e5218ff9c33c24b8efcf54f71accb7b055c"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.20,<0.21"
+loguru = ">=0.7.2,<0.8.0"
+numpy = [
+    {version = ">=1.21", markers = "python_version < \"3.12\""},
+    {version = ">=1.26", markers = "python_version >= \"3.12\""},
+]
+onnxruntime-gpu = ">=1.17.0,<2.0.0"
+requests = ">=2.31,<3.0"
+tokenizers = ">=0.15,<0.16"
 tqdm = ">=4.66,<5.0"
 
 [[package]]
@@ -602,13 +625,13 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
-    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
+    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
+    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
 ]
 
 [package.extras]
@@ -664,61 +687,61 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "grpcio"
-version = "1.63.0rc1"
+version = "1.63.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.63.0rc1)"]
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 [[package]]
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 [package.dependencies]
@@ -1450,13 +1465,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
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 [package.dependencies]
@@ -1478,9 +1493,9 @@ tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
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-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
 test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
@@ -1711,6 +1726,33 @@ 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 = "*"
+files = [
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+
+[package.dependencies]
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+flatbuffers = "*"
+numpy = ">=1.21.6"
+packaging = "*"
+protobuf = "*"
+sympy = "*"
+
 [[package]]
 name = "packaging"
 version = "24.0"
@@ -1797,28 +1839,29 @@ files = [
 
 [[package]]
 name = "platformdirs"
-version = "4.2.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.2.1"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
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 [package.extras]
@@ -1891,22 +1934,22 @@ files = [
 
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 python-versions = ">=3.8"
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 [[package]]
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+version = "2.7.1"
 description = "Data validation using Python type hints"
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 [package.dependencies]
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 typing-extensions = ">=4.6.1"
 
 [package.extras]
@@ -1966,90 +2009,90 @@ email = ["email-validator (>=2.0.0)"]
 
 [[package]]
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+    {file = "pyzmq-26.0.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e76654e9dbfb835b3518f9938e565c7806976c07b37c33526b574cc1a1050480"},
+    {file = "pyzmq-26.0.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:871587bdadd1075b112e697173e946a07d722459d20716ceb3d1bd6c64bd08ce"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d0a2d1bd63a4ad79483049b26514e70fa618ce6115220da9efdff63688808b17"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0270b49b6847f0d106d64b5086e9ad5dc8a902413b5dbbb15d12b60f9c1747a4"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:703c60b9910488d3d0954ca585c34f541e506a091a41930e663a098d3b794c67"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:74423631b6be371edfbf7eabb02ab995c2563fee60a80a30829176842e71722a"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4adfbb5451196842a88fda3612e2c0414134874bffb1c2ce83ab4242ec9e027d"},
+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3516119f4f9b8671083a70b6afaa0a070f5683e431ab3dc26e9215620d7ca1ad"},
+    {file = "pyzmq-26.0.3.tar.gz", hash = "sha256:dba7d9f2e047dfa2bca3b01f4f84aa5246725203d6284e3790f2ca15fba6b40a"},
 ]
 
 [package.dependencies]
@@ -2380,13 +2423,13 @@ resolved_reference = "a90cdd5925783c2b0ed3b8d39897cd4eaf942e2a"
 
 [[package]]
 name = "referencing"
-version = "0.34.0"
+version = "0.35.1"
 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-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"},
+    {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"},
 ]
 
 [package.dependencies]
@@ -2741,13 +2784,13 @@ 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 +2798,7 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "tokenizers"
@@ -2917,13 +2960,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.2"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
-    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
 ]
 
 [package.dependencies]
@@ -2937,18 +2980,18 @@ 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 +3085,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [extras]
 fastembed = ["fastembed"]
+fastembed-gpu = ["fastembed-gpu"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8"
-content-hash = "d9f322012dac6de2c4f537befddade3c059b8f2dcd9420ff742450739da4dc03"
+content-hash = "5a438b3f01f44b4b86d95f7c71e9bf3cae3a67316dc27cb657f4539e08570bb3"
diff --git a/pyproject.toml b/pyproject.toml
index 25b7d281..899e7940 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,7 +27,10 @@ grpcio-tools = ">=1.41.0"
 urllib3 = ">=1.26.14,<3"
 portalocker = "^2.7.0"
 fastembed = [
-    { version = "0.2.6", optional = true, python = "<3.13" }
+    { version = "0.2.7", optional = true, python = "<3.13" }
+]
+fastembed-gpu = [
+    { version = "0.2.7", optional = true, python = "<3.13" }
 ]
 
 [tool.poetry.group.dev.dependencies]
@@ -58,6 +61,7 @@ types-protobuf = "^4.21.0.5"
 
 [tool.poetry.extras]
 fastembed = ["fastembed"]
+fastembed-gpu = ["fastembed-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..89b48d8b 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
@@ -21,11 +21,12 @@
 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
+    from fastembed.common import OnnxProvider
 except ImportError:
     TextEmbedding = None
     SparseTextEmbedding = None
+    OnnxProvider = None
 SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = (
     {
         model["model"]: (model["dim"], models.Distance.COSINE)
@@ -51,8 +52,7 @@ 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 +75,7 @@ def set_model(
         max_length: Optional[int] = None,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> None:
         """
@@ -86,6 +87,9 @@ 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 +104,11 @@ 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 +117,7 @@ def set_sparse_model(
         embedding_model_name: Optional[str],
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
     ) -> None:
         """
         Set sparse embedding model to use for hybrid search over documents in combination with dense embeddings.
@@ -119,6 +128,9 @@ 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 +140,10 @@ 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 +170,7 @@ def _get_or_init_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> "TextEmbedding":
         if model_name in cls.embedding_models:
@@ -165,7 +181,11 @@ 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 +195,7 @@ def _get_or_init_sparse_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> "SparseTextEmbedding":
         if model_name in cls.sparse_embedding_models:
@@ -185,7 +206,11 @@ 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..d45d3378 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
@@ -10,11 +10,12 @@
 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
+    from fastembed.common import OnnxProvider
 except ImportError:
     TextEmbedding = None
     SparseTextEmbedding = None
+    OnnxProvider = None
 
 
 SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = (
@@ -45,8 +46,7 @@ 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 +70,7 @@ def set_model(
         max_length: Optional[int] = None,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> None:
         """
@@ -81,6 +82,9 @@ 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 +105,7 @@ 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 +115,7 @@ def set_sparse_model(
         embedding_model_name: Optional[str],
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
     ) -> None:
         """
         Set sparse embedding model to use for hybrid search over documents in combination with dense embeddings.
@@ -120,6 +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.
@@ -132,6 +141,7 @@ 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 +173,7 @@ def _get_or_init_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> "TextEmbedding":
         if model_name in cls.embedding_models:
@@ -179,6 +190,7 @@ 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 +201,7 @@ def _get_or_init_sparse_model(
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
+        providers: Optional[Sequence["OnnxProvider"]] = None,
         **kwargs: Any,
     ) -> "SparseTextEmbedding":
         if model_name in cls.sparse_embedding_models:
@@ -205,6 +218,7 @@ 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]