GGUF¶

Warning

Please note that GGUF support in vLLM is highly experimental and under-optimized at the moment, it might be incompatible with other features. Currently, you can use GGUF as a way to reduce memory footprint. If you encounter any issues, please report them to the vLLM team.

Warning

Currently, vllm only supports loading single-file GGUF models. If you have a multi-files GGUF model, you can use gguf-split tool to merge them to a single-file model.

To run a GGUF model with vLLM, you can use the repo_id:quant_type format to load directly from HuggingFace. For example, to load a Q4_K_M quantized model from unsloth/Qwen3-0.6B-GGUF:

# We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M --tokenizer Qwen/Qwen3-0.6B

You can also add --tensor-parallel-size 2 to enable tensor parallelism inference with 2 GPUs:

vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M \
   --tokenizer Qwen/Qwen3-0.6B \
   --tensor-parallel-size 2

Alternatively, you can download and use a local GGUF file:

wget https://huggingface.co/unsloth/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q4_K_M.gguf
vllm serve ./Qwen3-0.6B-Q4_K_M.gguf --tokenizer Qwen/Qwen3-0.6B

Warning

We recommend using the tokenizer from base model instead of GGUF model. Because the tokenizer conversion from GGUF is time-consuming and unstable, especially for some models with large vocab size.

GGUF assumes that HuggingFace can convert the metadata to a config file. In case HuggingFace doesn't support your model you can manually create a config and pass it as hf-config-path

# If your model is not supported by HuggingFace you can manually provide a HuggingFace compatible config path
vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M \
   --tokenizer Qwen/Qwen3-0.6B \
   --hf-config-path Qwen/Qwen3-0.6B

You can also use the GGUF model directly through the LLM entrypoint:

Code

from vllm import LLM, SamplingParams

# In this script, we demonstrate how to pass input to the chat method:
conversation = [
   {
      "role": "system",
      "content": "You are a helpful assistant",
   },
   {
      "role": "user",
      "content": "Hello",
   },
   {
      "role": "assistant",
      "content": "Hello! How can I assist you today?",
   },
   {
      "role": "user",
      "content": "Write an essay about the importance of higher education.",
   },
]

# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM using repo_id:quant_type format.
llm = LLM(
   model="unsloth/Qwen3-0.6B-GGUF:Q4_K_M",
   tokenizer="Qwen/Qwen3-0.6B",
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.chat(conversation, 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}")