diff --git a/launcher/src/main.rs b/launcher/src/main.rs
index 9c8abf8f..468ef63b 100644
--- a/launcher/src/main.rs
+++ b/launcher/src/main.rs
@@ -47,7 +47,6 @@ enum Quantization {
     /// Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better
     /// perplexity performance for you model
     BitsandbytesFP4,
-    /// [BETA]
     /// [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above
     /// This dtype has native ops should be the fastest if available.
     /// This is currently not the fastest because of local unpacking + padding to satisfy matrix
diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py
index 905af9fd..312a4482 100644
--- a/server/text_generation_server/utils/layers.py
+++ b/server/text_generation_server/utils/layers.py
@@ -210,13 +210,8 @@ class Fp8Linear(nn.Module):
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         qinput, scale = fp8_quantize(input)
-        seqlen = qinput.shape[0]
-        if seqlen % 16 != 0:
-            missing = 16 - seqlen % 16
-            qinput = F.pad(qinput, (0, 0, 0, missing), "constant", value=0)
         output, _ = torch._scaled_mm(qinput, self.qweight.t(), out_dtype=self.dtype,
                              scale_a=scale , scale_b=self.scale, bias=self.bias)
-        output = output[:seqlen]
         return output
 
 class Linear8bitLt(nn.Module):