diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md
index 622ec1cbc..3780c1f06 100644
--- a/torchao/quantization/README.md
+++ b/torchao/quantization/README.md
@@ -164,6 +164,117 @@ model = torch.compile(model, mode='max-autotune')
 model(input)
 ```
 
+## Affine Quantization
+Affine quantization refers to the type of quantization that maps from floating point numbers to quantized numbers (typically integer) with an affine transformation, i.e.: `quantized_val = float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data.
+
+### Quantization Primitives
+We used to have different quantize and dequantize operators for quantization with different granularities. But in the end these can all be expressed with a `block_size` argument with different settings, so we unified existing quant primitives to `choose_qparams_affine`, `quantize_affine` and `dequantize_affine` that can represent symmetric/asymmetric per tensor/channel/token/channel_group quantization, this can be used to implement the unified quantized tensor subclass.
+
+### Quantized Tensor Subclass
+We also have a unified quantized tensor subclass that implements how to get a quantized tensor from floating point tensor and what does it mean to call linear ops on an instance of the tensor, e.g. `F.linear` and `aten.addmm`, with this we could dispatch to different operators (e.g. `int4mm` op) based on device (cpu, cuda) and quantization settings (`int4`, `int8`) and also packing formats (e.g. format optimized for cpu int4 mm kernel)
+
+### Quantization Flow
+What we need to do afterwards is roughly the following
+
+```
+from torchao.dtypes.aqt import to_aq
+def apply_int8wo_quant(weight):
+    mapping_type = MappingType.SYMMETRIC
+    target_dtype = torch.int8
+    eps = torch.finfo(torch.float32).eps
+    zero_point_dtype = torch.int64
+    block_size = (1, weight.shape[1])
+    return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
+
+for n, m in model.named_modules():
+    if isinstance(m, torch.nn.Linear):
+        # optional filtering for module name, shape etc.
+        m.weight = nn.Parameter(apply_int8wo_quant(m.weight))
+        # note: quantization for activation need to be applied after the weight quantization
+        # quantization activation (needed by dynamic quantization)
+        # input_quant_func = apply_int8wo_quant  # specify how input activation is quantized
+        # m.weight = nn.Parameter(to_laq(m.weight, input_quant_func))
+```
+The model/tensor subclass should also be compatible with AOTI and torch.export, currently we can support
+`torch.export.export` and `torch.aot_compile` with the following workaround:
+```
+from torchao.quantization.utils import unwrap_tensor_subclass
+m_unwrapped = unwrap_tensor_subclass(m)
+
+
+# export
+m = torch.export.export(m_unwrapped, example_inputs).module()
+
+# aot_compile
+torch._export.aot_compile(m_unwrapped, example_inputs)
+```
+
+But we expect this will be integrated into the export path by default in the future.
+
+
+### Example
+Let's use int4 weight only quantization that's targeting tinygemm int4 weight only quantized matmul
+as an example:
+```python
+import torch
+from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain
+from torchao.dtypes import to_aq
+from torch._inductor.runtime.runtime_utils import do_bench_gpu
+import copy
+from torchao.quantization.quant_api import (
+    quantize,
+    get_apply_int4wo_quant,
+)
+
+class ToyLinearModel(torch.nn.Module):
+    def __init__(self, m=64, n=32, k=64):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(m, n, bias=False)
+        self.linear2 = torch.nn.Linear(n, k, bias=False)
+
+    def example_inputs(self, batch_size=1, dtype=torch.float32, device="cpu"):
+        return (torch.randn(batch_size, self.linear1.in_features, dtype=dtype, device=device),)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.linear2(x)
+        return x
+
+dtype = torch.bfloat16
+m = ToyLinearModel(1024, 1024, 1024).eval().to(dtype).to("cuda")
+m_bf16 = copy.deepcopy(m)
+example_inputs = m.example_inputs(dtype=dtype, device="cuda")
+
+m_bf16 = torch.compile(m_bf16, mode='max-autotune')
+# apply int4 weight only quant (compatible with tinygemm int4 weight only quant mm kernel in torchao)
+groupsize = 32
+m = quantize(m, get_apply_int4wo_quant(groupsize=groupsize))
+
+torch._inductor.config.force_fuse_int_mm_with_mul = True
+torch._inductor.config.use_mixed_mm = True
+
+# temporary workaround for tensor subclass + torch.compile
+from torchao.quantization.utils import unwrap_tensor_subclass
+m = unwrap_tensor_subclass(m)
+# compile the model to improve performance
+m = torch.compile(m, mode='max-autotune')
+
+# benchmark to see the speedup
+from torchao.utils import benchmark_model
+
+num_runs = 100
+torch._dynamo.reset()
+bf16_time = benchmark_model(m_bf16, num_runs, example_inputs[0])
+print(f"bf16 mean time: {bf16_time}")
+int4_time = benchmark_model(m, num_runs, example_inputs[0])
+print(f"int4 weight only quantized mean time: {int4_time}")
+print(f"speedup: {bf16_time / int4_time}")
+
+# output (1xA100 GPU machine)
+bf16 mean time: 71.457685546875
+int4 weight only quantized mean time: 31.4580908203125
+speedup: 2.2715200981216173
+```
 
 ## Notes