diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py
index bd4bcce1aa..7fdeb65d96 100644
--- a/torchao/quantization/quant_primitives.py
+++ b/torchao/quantization/quant_primitives.py
@@ -323,21 +323,23 @@ def dynamically_quantize_per_tensor(
 
 
 def quantize_activation_per_token_absmax(t):
-    n_bits = 8
     # if the shape of t is [B, N, K], the shape of scales will be [B, N, 1]
+    mapping_type = MappingType.SYMMETRIC
+    block_size = list(t.shape)
+    for i in range(len(block_size) - 1):
+        block_size[i] = 1
+    dtype = torch.int8
+    eps = 1e-5
+    scale, zero_point = choose_qparams_affine(t, mapping_type, block_size, dtype, eps=eps, scale_dtype=torch.float)
 
-    scales = t.abs().amax(dim=-1, keepdim=True)
-    if scales.dtype == torch.float16:
-        scales = (
-            scales.float()
-        )  # want float scales to avoid overflows for fp16, (bf16 has wide enough range)
-    q_max = 2 ** (n_bits - 1) - 1
-    scales = scales.clamp(min=1e-5).div(q_max)
     # Note: the original smoothquant does not clamp to qmin/qmax here,
     # but some of the tests with bfloat16 ended up with a flipped sign
     # if we don't clamp.  TODO(future) look into this further.
-    t = torch.round(t / scales).clamp(-127, 127).to(torch.int8)
-    return t, scales
+    quant_min = -127
+    quant_max = 127
+    quantized = quantize_affine(t, block_size, scale, zero_point, dtype, quant_min, quant_max)
+
+    return quantized, scale
 
 
 def dynamically_quantize_per_channel(x, quant_min, quant_max, target_dtype):
diff --git a/tutorials/quantize_vit/quant.json.gz b/tutorials/quantize_vit/quant.json.gz
index a207cefc5f..0b3200eeb5 100644
Binary files a/tutorials/quantize_vit/quant.json.gz and b/tutorials/quantize_vit/quant.json.gz differ