{ "hf_id": "OpenGVLab/InternVL3_5-8B", "meta": { "title": "InternVL3.5", "slug": "internvl3.5", "provider": "InternVL (OpenGVLab)", "description": "InternVL 3.5 vision-language models from Shanghai AI Lab with thinking-mode prompting", "date_added": "2025-09-20", "date_updated": "2026-04-17", "tasks": [ "multimodal" ], "related_recipes": [ "internlm/Intern-S1" ], "hardware": { "h100": "verified", "mi300x": "verified", "mi325x": "verified", "mi355x": "verified" } }, "recommended_command": { "hardware": "h200", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": {}, "docker_image": "vllm/vllm-openai:latest", "command": "vllm serve OpenGVLab/InternVL3_5-8B \\\n --trust-remote-code \\\n --tensor-parallel-size 1", "argv": [ "vllm", "serve", "OpenGVLab/InternVL3_5-8B", "--trust-remote-code", "--tensor-parallel-size", "1" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:latest OpenGVLab/InternVL3_5-8B \\\n --trust-remote-code \\\n --tensor-parallel-size 1", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "vllm/vllm-openai:latest", "OpenGVLab/InternVL3_5-8B", "--trust-remote-code", "--tensor-parallel-size", "1" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy โ€” works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "hopper", "compute_arch": "sm90", "display_name": "H200", "description": "NVIDIA H200 SXM 141 GB HBM3e ยท 8-GPU HGX node", "gpu_count": 8, "vram_gb": 1128, "multi_node": false }, "alternatives": { "multi_node_tp": "/OpenGVLab/InternVL3_5-8B/strategies/multi_node_tp.json", "kv_store_centralized_mooncake": "/OpenGVLab/InternVL3_5-8B/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/OpenGVLab/InternVL3_5-8B/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "h200": "/OpenGVLab/InternVL3_5-8B/hw/h200.json", "h100": "/OpenGVLab/InternVL3_5-8B/hw/h100.json", "b200": "/OpenGVLab/InternVL3_5-8B/hw/b200.json", "gb200": "/OpenGVLab/InternVL3_5-8B/hw/gb200.json", "b300": "/OpenGVLab/InternVL3_5-8B/hw/b300.json", "gb300": "/OpenGVLab/InternVL3_5-8B/hw/gb300.json", "mi300x": "/OpenGVLab/InternVL3_5-8B/hw/mi300x.json", "mi325x": "/OpenGVLab/InternVL3_5-8B/hw/mi325x.json", "mi350x": "/OpenGVLab/InternVL3_5-8B/hw/mi350x.json", "mi355x": "/OpenGVLab/InternVL3_5-8B/hw/mi355x.json" } }, "model": { "model_id": "OpenGVLab/InternVL3_5-8B", "min_vllm_version": "0.10.0", "architecture": "dense", "parameter_count": "8B", "active_parameters": "8B", "context_length": 40960, "base_args": [ "--trust-remote-code" ], "base_env": {}, "install": { "pip": { "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto" }, "docker": { "command": "docker pull vllm/vllm-openai:latest" } } }, "features": { "text_only": { "description": "Skip loading the vision encoder for text-only workloads โ€” frees VRAM for KV cache. Mutually exclusive with encoder_parallel.", "args": [ "--language-model-only" ] }, "encoder_parallel": { "description": "Run the vision encoder in data-parallel mode โ€” avoids TP comm overhead on the small encoder. Mutually exclusive with text_only.", "args": [ "--mm-encoder-tp-mode", "data" ] } }, "opt_in_features": [ "text_only", "encoder_parallel" ], "variants": { "default": { "precision": "bf16", "vram_minimum_gb": 19, "description": "BF16 weights for the 8B variant" } }, "kv_offload_support": { "offloading_cpu": "verified", "offloading_fs": "verified" }, "hardware_overrides": {}, "guide": "## Overview\n\n[InternVL3.5](https://github.com/OpenGVLab/InternVL) is a vision-language model developed\nby Shanghai AI Laboratory. It supports single-image and multi-image prompts, plus an\noptional \"thinking mode\" via a custom system prompt.\n\n## Prerequisites\n\n- Hardware: 1x GPU with >=20 GB VRAM (A100, L40S, H100, etc.)\n- vLLM >= 0.10.0\n\n### Install vLLM (CUDA)\n\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto\n```\n\n### Install vLLM (AMD ROCm MI300X/MI325X/MI355X)\n\n```bash\nuv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.14.1/rocm700\n```\n\n## Launch command\n\n```bash\nvllm serve OpenGVLab/InternVL3_5-8B --trust-remote-code\n```\n\nOn AMD:\n\n```bash\nexport VLLM_ROCM_USE_AITER=1\nvllm serve OpenGVLab/InternVL3_5-8B --trust-remote-code\n```\n\n## Client Usage\n\nSingle image:\n\n```python\nfrom openai import OpenAI\nclient = OpenAI(api_key=\"\", base_url=\"http://0.0.0.0:8000/v1\")\nmodel_name = client.models.list().data[0].id\n\nresponse = client.chat.completions.create(\n model=model_name,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"Describe the image.\"},\n {\"type\": \"image_url\", \"image_url\": {\"url\": \"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg\"}},\n ],\n }],\n temperature=0.0,\n)\nprint(response.choices[0].message.content)\n```\n\n## Thinking Mode\n\nSet a thinking system prompt and use `temperature=0.6` to mitigate repetition:\n\n```python\nTHINKING_SYSTEM_PROMPT = \"\"\"\nYou are an AI assistant that rigorously follows this response protocol:\n\n1. First, conduct a detailed analysis of the question. Consider different angles, potential\nsolutions, and reason through the problem step-by-step. Enclose this entire thinking process\nwithin and tags.\n\n2. After the thinking section, provide a clear, concise, and direct answer to the user's\nquestion. Separate the answer from the think section with a newline.\n\"\"\".strip()\n```\n\n## References\n\n- [InternVL3.5-8B](https://huggingface.co/OpenGVLab/InternVL3_5-8B)\n- [InternVL GitHub](https://github.com/OpenGVLab/InternVL)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/OpenGVLab/InternVL3_5-8B.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/OpenGVLab/InternVL3_5-8B.yaml", "overlays": [] } }