InternVL

March 19, 2026 ยท View on GitHub

LMDeploy supports the following InternVL series of models, which are detailed in the table below:

ModelSizeSupported Inference Engine
InternVL13B-19BTurboMind
InternVL1.52B-26BTurboMind, PyTorch
InternVL24BPyTorch
InternVL21B-2B, 8B-76BTurboMind, PyTorch
InternVL2.5/2.5-MPO/31B-78BTurboMind, PyTorch
Mono-InternVL2BPyTorch

The next chapter demonstrates how to deploy an InternVL model using LMDeploy, with InternVL2-8B as an example.

Installation

Please install LMDeploy by following the installation guide, and install other packages that InternVL2 needs

pip install timm
# It is recommended to find the whl package that matches the environment from the releases on https://github.com/Dao-AILab/flash-attention.
pip install flash-attn

Or, you can build a docker image to set up the inference environment. If the CUDA version on your host machine is >=12.4, you can run:

docker build --build-arg CUDA_VERSION=cu12 -t openmmlab/lmdeploy:internvl . -f ./docker/InternVL_Dockerfile

Otherwise, you can go with:

git clone https://github.com/InternLM/lmdeploy.git
cd lmdeploy
docker build --build-arg CUDA_VERSION=cu11 -t openmmlab/lmdeploy:internvl . -f ./docker/InternVL_Dockerfile

Offline inference

The following sample code shows the basic usage of VLM pipeline. For detailed information, please refer to VLM Offline Inference Pipeline

from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('OpenGVLab/InternVL2-8B')

image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
response = pipe((f'describe this image', image))
print(response)

More examples are listed below:

multi-image multi-round conversation, combined images 
from lmdeploy import pipeline, GenerationConfig
from lmdeploy.vl.constants import IMAGE_TOKEN

pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')
messages = [
    dict(role='user', content=[
        dict(type='text', text=f'{IMAGE_TOKEN}{IMAGE_TOKEN}\nDescribe the two images in detail.'),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image1.jpg')),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image2.jpg'))
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
multi-image multi-round conversation, separate images 
from lmdeploy import pipeline, GenerationConfig
from lmdeploy.vl.constants import IMAGE_TOKEN

pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')
messages = [
    dict(role='user', content=[
        dict(type='text', text=f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\nDescribe the two images in detail.'),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image1.jpg')),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image2.jpg'))
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
video multi-round conversation 
import numpy as np
from lmdeploy import pipeline, GenerationConfig
from decord import VideoReader, cpu
from lmdeploy.vl.constants import IMAGE_TOKEN
from lmdeploy.vl import encode_image_base64
from PIL import Image
pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')


def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = np.array([
        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
        for idx in range(num_segments)
    ])
    return frame_indices


def load_video(video_path, bound=None, num_segments=32):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())
    pixel_values_list, num_patches_list = [], []
    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
    imgs = []
    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
        imgs.append(img)
    return imgs


video_path = 'red-panda.mp4'
imgs = load_video(video_path, num_segments=8)

question = ''
for i in range(len(imgs)):
    question = question + f'Frame{i+1}: {IMAGE_TOKEN}\n'

question += 'What is the red panda doing?'

content = [{'type': 'text', 'text': question}]
for img in imgs:
    content.append({'type': 'image_url', 'image_url': {'max_dynamic_patch': 1, 'url': f'data:image/jpeg;base64,{encode_image_base64(img)}'}})

messages = [dict(role='user', content=content)]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='Describe this video in detail. Don\'t repeat.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

Online serving

You can launch the server by the lmdeploy serve api_server CLI:

lmdeploy serve api_server OpenGVLab/InternVL2-8B

You can also start the service using the aforementioned built docker image:

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    -p 23333:23333 \
    --ipc=host \
    openmmlab/lmdeploy:internvl \
    lmdeploy serve api_server OpenGVLab/InternVL2-8B

The docker compose is another option. Create a docker-compose.yml configuration file in the root directory of the lmdeploy project as follows:

version: '3.5'

services:
  lmdeploy:
    container_name: lmdeploy
    image: openmmlab/lmdeploy:internvl
    ports:
      - "23333:23333"
    environment:
      HUGGING_FACE_HUB_TOKEN: <secret>
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    stdin_open: true
    tty: true
    ipc: host
    command: lmdeploy serve api_server OpenGVLab/InternVL2-8B
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: "all"
              capabilities: [gpu]

Then, you can execute the startup command as below:

docker-compose up -d

If you find the following logs after running docker logs -f lmdeploy, it means the service launches successfully.

HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
INFO:     Started server process [2439]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on  http://0.0.0.0:23333  (Press CTRL+C to quit)

The arguments of lmdeploy serve api_server can be reviewed in detail by lmdeploy serve api_server -h.

More information about api_server as well as how to access the service can be found from here