Instructions to use 88plug/Kimi-VL-A3B-Thinking-2506-W8A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 88plug/Kimi-VL-A3B-Thinking-2506-W8A16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="88plug/Kimi-VL-A3B-Thinking-2506-W8A16", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("88plug/Kimi-VL-A3B-Thinking-2506-W8A16", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 88plug/Kimi-VL-A3B-Thinking-2506-W8A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "88plug/Kimi-VL-A3B-Thinking-2506-W8A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "88plug/Kimi-VL-A3B-Thinking-2506-W8A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/88plug/Kimi-VL-A3B-Thinking-2506-W8A16

SGLang

How to use 88plug/Kimi-VL-A3B-Thinking-2506-W8A16 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 "88plug/Kimi-VL-A3B-Thinking-2506-W8A16" \
    --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": "88plug/Kimi-VL-A3B-Thinking-2506-W8A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "88plug/Kimi-VL-A3B-Thinking-2506-W8A16" \
        --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": "88plug/Kimi-VL-A3B-Thinking-2506-W8A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use 88plug/Kimi-VL-A3B-Thinking-2506-W8A16 with Docker Model Runner:
```
docker model run hf.co/88plug/Kimi-VL-A3B-Thinking-2506-W8A16
```

Load path (important)

These weights are compressed-tensors (pack-quantized / int-quantized).

Runtime	Supported
vLLM ≥ 0.21	Yes — preferred (auto-detect CT; no `--quantization` flag)
transformers + `compressed-tensors`	Yes for many text models; multimodal may need custom code
Text Generation Inference (TGI)	Not supported for these CT packs
Hugging Face Inference Widget	Often fails — use vLLM locally instead

# Preferred
vllm serve 88plug/<ModelName> --trust-remote-code

Do not deploy via TGI “text-generation-inference” paths — that backend does not load our CT format and produces opaque worker/load errors.

Kimi-VL-A3B-Thinking-2506-W8A16

INT8 post-training quantization of moonshotai/Kimi-VL-A3B-Thinking-2506 — a 16B MoE vision-language model with thinking/reasoning. Language-model Linear layers quantized W8A16; vision tower and merger kept BF16.

At a Glance

Property	Value
Base model	`moonshotai/Kimi-VL-A3B-Thinking-2506`
Release tier	Provisional (datafree RTN — re-quant scheduled)
Quant method	datafree RTN W8A16 (weight-only INT8)
FLAC status	Not measured (T+7d milestone)
Architecture	Vision encoder + merger + MoE language model
Quant format	compressed-tensors (native vLLM)
Quantized	language_model Linear layers
Kept BF16	vision encoder, merger, projector, norms, router
Disk size	~17 GB
Min GPU	1× RTX A6000 48GB (recommended)

Quick Start

Tested with vLLM v0.21.0. Weights are in compressed-tensors format — vLLM detects quantization automatically.

docker run --gpus device=0 -p 8080:8080 \
  vllm/vllm-openai:v0.21.0-cu129-ubuntu2404 vllm serve \
  88plug/Kimi-VL-A3B-Thinking-2506-W8A16 \
  --trust-remote-code \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.90

Quantization Details

Parameter	Value
Method	llmcompressor datafree
Weight bits	8 (symmetric, group_size 128)
Activation	16-bit
`actorder`	N/A (datafree)
Recipe	`recipe.yaml` in repo

Benchmarks

Benchmarks in progress.

License

MIT (per base model).

About

88plug AI Lab ships compressed-tensors quantizations for native vLLM v0.21.0+ deployment.

This release: Provisional tier — datafree RTN (weight-only rounding, no calibration corpus). A gold AutoRound re-quant is scheduled; 88plug architecture forbids new provisional W4A16 uploads.

Browse all releases → huggingface.co/88plug