diff --git a/server/text_generation_server/layers/fp8.py b/server/text_generation_server/layers/fp8.py
index c626ddb8..18a40afa 100644
--- a/server/text_generation_server/layers/fp8.py
+++ b/server/text_generation_server/layers/fp8.py
@@ -76,9 +76,19 @@ def normalize_e4m3fn_to_e4m3fnuz(
 
 
 def fp8_quantize(
-    weight, scale=None, scale_upper_bound=None, qdtype=torch.float8_e4m3fn, scalar=False
+    weight: torch.Tensor,
+    scale: Optional[torch.Tensor] = None,
+    scale_upper_bound: Optional[torch.Tensor] = None,
+    qdtype: torch.dtype = torch.float8_e4m3fn,
+    scalar: bool = False,
 ):
-    if FBGEMM_DYN_AVAILABLE and not scalar:
+    """
+    This function returns a reciprocal of the scale, so that a tensor can be unscaled
+    by multiplying it with the returned scale. If a scale is given through the `scale`
+    argument, it must also be a reciprocal (so that scales from an FP8 checkpoint can
+    be used without modification).
+    """
+    if FBGEMM_DYN_AVAILABLE and not scalar and not scale:
         qweight, scale = torch.ops.fbgemm.quantize_fp8_per_row(
             weight, bs=None, scale_ub=scale_upper_bound, output_dtype=qdtype
         )
@@ -90,15 +100,18 @@ def fp8_quantize(
     if scale is None:
         # Calculate the scale as dtype max divided by absmax
         scale = finfo.max / weight.abs().max().clamp(min=1e-12, max=scale_upper_bound)
+        # scale and clamp the tensor to bring it to
+        # the representative range of float8 data type
+        # (as default cast is unsaturated)
+        qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max)
+        scale = scale.float().reciprocal()
+    else:
+        # Use reciprocal to avoid more expensive division.
+        qweight = (weight * scale.reciprocal()).clamp(min=finfo.min, max=finfo.max)
 
-    # scale and clamp the tensor to bring it to
-    # the representative range of float8 data type
-    # (as default cast is unsaturated)
-    qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max)
     # Return both float8 data and the inverse scale (as float),
     # as both required as inputs to torch._scaled_mm
     qweight = qweight.to(qdtype)
-    scale = scale.float().reciprocal()
 
     if SYSTEM == "rocm":
         qweight, scale, _ = normalize_e4m3fn_to_e4m3fnuz(qweight, scale)
@@ -307,9 +320,7 @@ class Fp8Linear(torch.nn.Module):
         self.dtype = dtype
         self.qweight = qweight
         self.scale = scale.float()
-        self.input_scale = (
-            input_scale.float().reciprocal() if input_scale is not None else None
-        )
+        self.input_scale = input_scale.float() if input_scale is not None else None
 
         if FBGEMM_MM_AVAILABLE:
             self.scale_upper_bound = (