Instructions to use QuantTrio/DeepSeek-V3.2-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/DeepSeek-V3.2-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantTrio/DeepSeek-V3.2-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("QuantTrio/DeepSeek-V3.2-AWQ", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuantTrio/DeepSeek-V3.2-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/DeepSeek-V3.2-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/DeepSeek-V3.2-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/DeepSeek-V3.2-AWQ

SGLang

How to use QuantTrio/DeepSeek-V3.2-AWQ with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantTrio/DeepSeek-V3.2-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/DeepSeek-V3.2-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantTrio/DeepSeek-V3.2-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/DeepSeek-V3.2-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/DeepSeek-V3.2-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/DeepSeek-V3.2-AWQ
```

Did someone get it running on 4 NVIDIA RTX PRO 6000 Blackwell (96 GB) GPUs?

by FabianHeller - opened Dec 4, 2025

Discussion

FabianHeller

Dec 4, 2025

I have 4 NVIDIA RTX PRO 6000 Blackwell GPUs, does it fit? If yes, how much KV cache is left? If yes, what settings did you use? I am running at the moment GLM-4.6-FP8 with SGLang, there I still get 160k context length with FP8 KV cache quantization.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment