diff --git a/examples/quantization_w8a8_fp8/kimi_linear_fp8.py b/examples/quantization_w8a8_fp8/kimi_linear_fp8.py
new file mode 100644
index 000000000..2df3ad2f1
--- /dev/null
+++ b/examples/quantization_w8a8_fp8/kimi_linear_fp8.py
@@ -0,0 +1,45 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.utils import dispatch_for_generation
+
+MODEL_ID = "//proving-grounds/engine/hub_cache/models--moonshotai--Kimi-Linear-48B-A3B-Instruct/snapshots/fd1de6347c9d3896f6df8edc529c68942bdd58f6"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with channel-wise quantization
+#   * quantize the activations to fp8 with dynamic token activations
+# NOTE: only datafree quantization is supported for Qwen3-VL-MoE currently
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="FP8_DYNAMIC",
+    ignore=[
+        "re:.*lm_head",
+        "re:.*gate$",
+        "re:.*self_attn$",
+    ],
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+"""
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+"""
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = "/proving-grounds/engine/hub_cache/Kimi-Linear-48B-A3B-Instruct" + "-FP8-DYNAMIC"
+model.save_pretrained(SAVE_DIR)
+tokenizer.save_pretrained(SAVE_DIR)