diff --git a/litgpt/deploy/serve.py b/litgpt/deploy/serve.py
index 5b54f86b40..9df48ad98d 100644
--- a/litgpt/deploy/serve.py
+++ b/litgpt/deploy/serve.py
@@ -4,7 +4,6 @@
 from litgpt.utils import check_valid_checkpoint_dir
 
 import lightning as L
-from lightning.fabric.plugins import BitsandbytesPrecision
 import torch
 from litserve import LitAPI, LitServer
 
@@ -20,7 +19,6 @@ class SimpleLitAPI(LitAPI):
     def __init__(self,
                  checkpoint_dir: Path,
                  precision: Optional[str] = None,
-                 quantize: Optional[Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8"]] = None,
                  temperature: float = 0.8,
                  top_k: int = 50,
                  max_new_tokens: int = 50) -> None:
@@ -28,7 +26,6 @@ def __init__(self,
         super().__init__()
         self.checkpoint_dir = checkpoint_dir
         self.precision = precision
-        self.quantize = quantize
         self.temperature = temperature
         self.top_k = top_k
         self.max_new_tokens = max_new_tokens
@@ -40,19 +37,11 @@ def setup(self, device: str) -> None:
         torch.set_float32_matmul_precision("high")
 
         precision = self.precision or get_default_supported_precision(training=False)
-        plugins = None
-        if self.quantize is not None and self.quantize.startswith("bnb."):
-            if "mixed" in self.precision:
-                raise ValueError("Quantization and mixed precision is not supported.")
-            dtype = {"16-true": torch.float16, "bf16-true": torch.bfloat16, "32-true": torch.float32}[precision]
-            plugins = BitsandbytesPrecision(self.quantize[4:], dtype)
-            precision = None
 
         fabric = L.Fabric(
             accelerator=device.type,
             devices=1 if device.type=="cpu" else [device.index], # TODO: Update once LitServe supports "auto"
             precision=precision,
-            plugins=plugins,
         )
         checkpoint_path = self.checkpoint_dir / "lit_model.pth"
         self.tokenizer = Tokenizer(self.checkpoint_dir)
@@ -106,7 +95,6 @@ def encode_response(self, output: torch.Tensor) -> Dict[str, Any]:
 def run_server(
     checkpoint_dir: Path = Path("checkpoints"),
     precision: Optional[str] = None,
-    quantize: Optional[Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8"]] = None,
     temperature: float = 0.8,
     top_k: int = 200,
     max_new_tokens: int = 50,
@@ -120,10 +108,6 @@ def run_server(
         checkpoint_dir: The checkpoint directory to load the model from.
         precision: Optional precision setting to instantiate the model weights in. By default, this will
             automatically be inferred from the metadata in the given ``checkpoint_dir`` directory.
-        quantize: Whether to quantize the model and using which method:
-            - bnb.nf4, bnb.nf4-dq, bnb.fp4, bnb.fp4-dq: 4-bit quantization from bitsandbytes
-            - bnb.int8: 8-bit quantization from bitsandbytes
-            for more details, see https://github.com/Lightning-AI/litgpt/blob/main/tutorials/quantize.md
         temperature: Temperature setting for the text generation. Value above 1 increase randomness.
             Values below 1 decrease randomness.
         top_k: The size of the pool of potential next tokens. Values larger than 1 result in more novel
@@ -139,7 +123,6 @@ def run_server(
         SimpleLitAPI(
             checkpoint_dir=checkpoint_dir,
             precision=precision,
-            quantize=quantize,
             temperature=temperature,
             top_k=top_k,
             max_new_tokens=max_new_tokens,