diff --git a/server/text_generation_server/models/custom_modeling/mamba_modeling.py b/server/text_generation_server/models/custom_modeling/mamba_modeling.py
index 0c019212..410a254a 100644
--- a/server/text_generation_server/models/custom_modeling/mamba_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/mamba_modeling.py
@@ -115,16 +115,61 @@ class MambaBlock(nn.Module):
         return attn_outputs, conv_state, last_state
 
     def step(self, hidden_states, conv_state, ssm_state):
-        # only support decoding with 1 token at a time
-        xz = self.in_proj(hidden_states.view((1, -1)))
-        x, z = xz.chunk(2, dim=-1)  # (B D)
-        x = causal_conv1d_update(
-            x,
-            conv_state, 
-            self.conv1d.weight.view(self.conv1d.weight.size(0), -1),
-            self.conv1d.bias,
-            self.activation,
-        )
+        _xz = self.in_proj(hidden_states)
+        _x, _z = _xz.chunk(2, dim=-1)  # (B D)
+        conv_state_new = torch.cat([conv_state, _x.transpose(1,2)], dim=-1)
+        conv_out = causal_conv1d_fn( x=conv_state_new, weight=self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2)), bias=self.conv1d.bias, activation=self.activation)
+        conv_state = conv_state_new[:, :, 1:]
+
+        handle_batched = False
+        if handle_batched:
+            bsz, seqlen, dim = hidden_states.shape
+            # empty output tensor for the loop
+            output_tensor = torch.zeros(
+                (bsz, seqlen, dim),
+                device=hidden_states.device, 
+                dtype=hidden_states.dtype
+            )
+
+            for i in range(0, bsz):
+                x = conv_out[:,:,i]
+                z = _z[:, i, :]
+                x_db = self.x_proj(x)
+                dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)
+                dt = self.dt_proj_no_bias(dt)
+                dt = F.softplus(dt + self.dt_proj.bias).view((dt.size(1), -1))
+                dA = torch.exp(dt * self.negA)
+                dB = dt * B.view(-1, B.size(-1))
+                x_shape = (-1, x.size(-1), 1)
+                # ssm_state = (ssm_state * dA + dB * x.view(x_shape)) 
+                ssm_state[i] = (ssm_state[i] * dA + dB * x)#.view(x_shape))
+                c_shape = (C.size(0), C.size(1), -1)
+                out_mm_shape = (C.size(0), -1)
+                out = torch.matmul(ssm_state.to(C.dtype), C.view(c_shape)).view(out_mm_shape)
+                # in-place ops
+                out.add_((x * self.D).to(out.dtype))
+                out.mul_(F.silu(z))
+                out = self.out_proj(out)
+                output_tensor[i] = out
+
+            return output_tensor, conv_state, ssm_state
+
+        # TODO: remove this code only left for reference
+        # # only support decoding with 1 token at a time
+        # xz = self.in_proj(hidden_states.view((1, -1)))
+        # x, z = xz.chunk(2, dim=-1)  # (B D)
+        # x = causal_conv1d_update(
+        #     x,
+        #     conv_state, 
+        #     self.conv1d.weight.view(self.conv1d.weight.size(0), -1),
+        #     self.conv1d.bias,
+        #     self.activation,
+        # )
+
+        # TODO: prefer using batched logic in all cases 
+        # this just pulls the last element of the batch 
+        x = conv_out[:,:,-1]
+        z = _z[:, -1, :]
         x_db = self.x_proj(x)
         dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)
         dt = self.dt_proj_no_bias(dt)