diff --git a/docs/source/features/quantization/auto_awq.md b/docs/source/features/quantization/auto_awq.md
index 7001ec91467f..b703d0195319 100644
--- a/docs/source/features/quantization/auto_awq.md
+++ b/docs/source/features/quantization/auto_awq.md
@@ -3,7 +3,7 @@
 # AutoAWQ
 
 To create a new 4-bit quantized model, you can leverage [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
-Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
+Quantization reduces the model's precision from BF16/FP16 to INT4 which effectively reduces the total model memory footprint.
 The main benefits are lower latency and memory usage.
 
 You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?sort=trending&search=awq).
diff --git a/docs/source/features/quantization/gptqmodel.md b/docs/source/features/quantization/gptqmodel.md
new file mode 100644
index 000000000000..34adf6512b7e
--- /dev/null
+++ b/docs/source/features/quantization/gptqmodel.md
@@ -0,0 +1,83 @@
+(gptqmodel)=
+
+# GPTQModel
+
+To create a new 4-bit or 8-bit GPTQ quantized model, you can leverage [GPTQModel](https://github.com/ModelCloud/GPTQModel) from ModelCloud.AI.
+
+Quantization reduces the model's precision from BF16/FP16 (16-bits) to INT4 (4-bits) or INT8 (8-bits) which significantly reduces the
+total model memory footprint while at-the-same-time increasing inference performance.
+
+Compatible GPTQModel quantized models can leverage the `Marlin` and `Machete` vLLM custom kernels to maximize batching
+transactions-per-second `tps` and token-latency performance for both Ampere (A100+) and Hopper (H100+) Nvidia GPUs.
+These two kernels are highly optimized by vLLM and NeuralMagic (now part of Redhat) to allow world-class inference performance of quantized GPTQ
+models.
+
+GPTQModel is one of the few quantization toolkits in the world that allows `Dynamic` per-module quantization where different layers and/or modules within a llm model can be further optimized with custom quantization parameters. `Dynamic` quantization
+is fully integrated into vLLM and backed up by support from the ModelCloud.AI team. Please refer to [GPTQModel readme](https://github.com/ModelCloud/GPTQModel?tab=readme-ov-file#dynamic-quantization-per-module-quantizeconfig-override)
+for more details on this and other advanced features.
+
+You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?sort=trending&search=gptq).
+
+```console
+pip install -U gptqmodel --no-build-isolation -v
+```
+
+After installing GPTQModel, you are ready to quantize a model. Please refer to the [GPTQModel readme](https://github.com/ModelCloud/GPTQModel/?tab=readme-ov-file#quantization) for further details.
+
+Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
+
+```python
+from datasets import load_dataset
+from gptqmodel import GPTQModel, QuantizeConfig
+
+model_id = "meta-llama/Llama-3.2-1B-Instruct"
+quant_path = "Llama-3.2-1B-Instruct-gptqmodel-4bit"
+
+calibration_dataset = load_dataset(
+    "allenai/c4",
+    data_files="en/c4-train.00001-of-01024.json.gz",
+    split="train"
+  ).select(range(1024))["text"]
+
+quant_config = QuantizeConfig(bits=4, group_size=128)
+
+model = GPTQModel.load(model_id, quant_config)
+
+# increase `batch_size` to match gpu/vram specs to speed up quantization
+model.quantize(calibration_dataset, batch_size=2)
+
+model.save(quant_path)
+```
+
+To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
+
+```console
+python examples/offline_inference/llm_engine_example.py --model DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
+```
+
+GPTQModel quantized models are also supported directly through the LLM entrypoint:
+
+```python
+from vllm import LLM, SamplingParams
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9)
+
+# Create an LLM.
+llm = LLM(model="DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2")
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
diff --git a/docs/source/features/quantization/index.md b/docs/source/features/quantization/index.md
index 1c98620aa214..65f438f599f1 100644
--- a/docs/source/features/quantization/index.md
+++ b/docs/source/features/quantization/index.md
@@ -12,6 +12,7 @@ supported_hardware
 auto_awq
 bnb
 gguf
+gptqmodel
 int4
 int8
 fp8