diff --git a/examples/quantizing_moe/deepseek_moe_w4a16.py b/examples/quantizing_moe/deepseek_moe_w4a16.py
new file mode 100644
index 000000000..bd1f95ead
--- /dev/null
+++ b/examples/quantizing_moe/deepseek_moe_w4a16.py
@@ -0,0 +1,121 @@
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
+
+# select a Mixture of Experts model for quantization
+MODEL_ID = "deepseek-ai/DeepSeek-V2.5"
+
+# adjust based off number of desired GPUs
+# if not enough memory is available, some layers will automatically be offlaoded to cpu
+device_map = calculate_offload_device_map(
+    MODEL_ID,
+    reserve_for_hessians=True,
+    num_gpus=2,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+    MODEL_ID, device_map=device_map, torch_dtype=torch.bfloat16, trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Select calibration dataset.
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+# define a llmcompressor recipe for W416 quantization
+# since the MoE gate layers are sensitive to quantization, we add them to the ignore
+# list so they remain at full precision
+recipe = "deepseek_recipe_w4a16.yaml"
+
+SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16"
+
+
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    save_compressed=True,
+    output_dir=SAVE_DIR,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+
+
+# Run the model on vLLM
+try:
+    from vllm import LLM, SamplingParams
+
+    vllm_installed = True
+except ImportError:
+    vllm_installed = False
+
+if vllm_installed:
+    print("vLLM installed, running using vLLM")
+    sampling_params = SamplingParams(temperature=0.80, top_p=0.95)
+    llm = LLM(
+        model=SAVE_DIR,
+        tensor_parallel_size=2,
+        trust_remote_code=True,
+        max_model_len=1042,
+        dtype=torch.half,
+    )
+    prompts = [
+        "The capital of France is",
+        "The president of the US is",
+        "My name is",
+    ]
+
+    outputs = llm.generate(prompts, sampling_params)
+    print("================= vLLM GENERATION ======================")
+    for output in outputs:
+        assert output
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print("PROMPT", prompt)
+        print("GENERATED TEXT", generated_text)
diff --git a/examples/quantizing_moe/deepseek_recipe_w4a16.yaml b/examples/quantizing_moe/deepseek_recipe_w4a16.yaml
new file mode 100644
index 000000000..05e294365
--- /dev/null
+++ b/examples/quantizing_moe/deepseek_recipe_w4a16.yaml
@@ -0,0 +1,9 @@
+quant_stage:
+  quant_modifiers:
+    GPTQModifier:
+      sequential_update: true
+      ignore: [lm_head, "re:.*mlp.gate$"]
+      config_groups:
+        group_0:
+          weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
+          targets: [Linear]