add mx and nv inference example to README.md

torchao/prototype/mx_formats/README.md

@@ -70,7 +70,59 @@ quantize_(m, config)
 
 ## MX inference
 
-Coming soon!
+```python
+import copy
+
+import torch
+import torch.nn as nn
+from torchao.quantization import quantize_
+from torchao.prototype.mx_formats.config import (
+    MXGemmKernelChoice,
+)
+from torchao.prototype.mx_formats.inference_workflow import (
+    MXFPInferenceConfig,
+    NVFP4InferenceConfig,
+    NVFP4MMConfig,
+)
+
+m = nn.Linear(32, 128, bias=False, dtype=torch.bfloat16, device="cuda")
+x = torch.randn(128, 32, device="cuda", dtype=torch.bfloat16)
+
+# mxfp8
+
+m_mxfp8 = copy.deepcopy(m)
+config = MXFPInferenceConfig(
+    activation_dtype=torch.float8_e4m3fn,
+    weight_dtype=torch.float8_e4m3fn,
+    gemm_kernel_choice=MXGemmKernelChoice.CUBLAS,
+)
+quantize_(m_mxfp8, config=config)
+m_mxfp8 = torch.compile(m_mxfp8, fullgraph=True)
+y_mxfp8 = m_mxfp8(x)
+
+# mxfp4
+
+m_mxfp4 = copy.deepcopy(m)
+config = MXFPInferenceConfig(
+    activation_dtype=torch.float4_e2m1fn_x2,
+    weight_dtype=torch.float4_e2m1fn_x2,
+    gemm_kernel_choice=MXGemmKernelChoice.CUTLASS,
+)
+quantize_(m_mxfp4, config=config)
+m_mxfp4 = torch.compile(m_mxfp4, fullgraph=True)
+y_mxfp4 = m_mxfp4(x)
+
+# nvfp4
+
+m_nvfp4 = copy.deepcopy(m)
+config = NVFP4InferenceConfig(
+    mm_config=NVFP4MMConfig.DYNAMIC,
+    use_dynamic_per_tensor_scale=True,
+)
+quantize_(m_nvfp4, config=config)
+m_nvfp4 = torch.compile(m_nvfp4, fullgraph=True)
+y_nvfp4 = m_nvfp4(x)
+```
 
 ## MXTensor