Model reference · open weights

InternVL2

LLMs OpenGVLab Vision + text 6 builds Open weights 649k dl/mo

InternVL2 is an open-weight language model from OpenGVLab. InternVL2-4B (BF16) weighs 8.3 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.

  • InternVL2-2B is a 2.2 billion parameter multimodal large language model developed by OpenGVLab for image-text-to-text tasks.
  • The model supports multilingual inputs and is trained with an 8k context window to handle long texts, multiple images, and videos.
  • It is released under the MIT license.

Summary of the OpenGVLab/InternVL2-2B model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released byOpenGVLab
Released2024-06-27
Parameters2.2B
VRAM8.3 GB for the weights

What it runs on

Memory and cards for InternVL2-4B (BF16)

8.3 GBweights, file size
393 MBcache per 1K tokens
1.9 GBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB——3K11.6 GB
RTX 4060 Ti 16 GB1—13K15.4 GB
RTX 3090 24 GB4132K23.4 GB
RTX 4090 24 GB4132K23.4 GB
RTX 5090 32 GB6151K31.0 GB
L40S 48 GB10283K44.0 GB
A100 80 GB215all 128K78.2 GB
H100 80 GB205all 128K78.1 GB
RTX PRO 6000 Blackwell 96 GB256all 128K93.8 GB
DGX Spark (GB10) 128 GB unified297all 128K107 GB
H200 141 GB389all 128K138 GB
B200 180 GB5012all 128K176 GB
2× RTX 3060 12 GB
tensor parallel
3—27K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
5146K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
10286K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
10286K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
153124K31.0 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
113.4 GB23.0 GB
526.3 GB74.6 GB
835.9 GB113 GB
1661.7 GB216 GB
32113 GB422 GB
64216 GB835 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
InternVL2-2B ↗ 2.2BBF16 4.4 GBRTX 3060 12 GB
InternVL2-1B ↗ 938MBF16 1.9 GBRTX 3060 12 GB
InternVL2-26B ↗ 25.5BBF16 51.0 GBH100 80 GB
InternVL2-8B ↗ 8.1BBF16 16.2 GB2× RTX 3060 12 GB
InternVL2-4B (above) ↗ 4.1BBF16 8.3 GBRTX 4060 Ti 16 GB
InternVL2-2B-AWQ ↗ —AWQ 2.2 GBRTX 3060 12 GB

From the model card

What OpenGVLab says about InternVL2

Read the model card

[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 Mini-InternVL] [📜 InternVL 2.5]

[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]

Introduction

We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of instruction-tuned models, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-2B model.

Compared to the state-of-the-art open-source multimodal large language models, InternVL 2.0 surpasses most open-source models. It demonstrates competitive performance on par with proprietary commercial models across various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal capabilities.

InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.

Model NameVision PartLanguage PartHF LinkMS Link
InternVL2-1BInternViT-300M-448pxQwen2-0.5B-Instruct🤗 link🤖 link
InternVL2-2BInternViT-300M-448pxinternlm2-chat-1_8b🤗 link🤖 link
InternVL2-4BInternViT-300M-448pxPhi-3-mini-128k-instruct🤗 link🤖 link
InternVL2-8BInternViT-300M-448pxinternlm2_5-7b-chat🤗 link🤖 link
InternVL2-26BInternViT-6B-448px-V1-5internlm2-chat-20b🤗 link🤖 link
InternVL2-40BInternViT-6B-448px-V1-5Nous-Hermes-2-Yi-34B🤗 link🤖 link
InternVL2-Llama3-76BInternViT-6B-448px-V1-5Hermes-2-Theta-Llama-3-70B🤗 link🤖 link

Model Details

InternVL 2.0 is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. InternVL2-2B consists of InternViT-300M-448px, an MLP projector, and internlm2-chat-1_8b.

Performance

Image Benchmarks

BenchmarkPaliGemma-3BMini-InternVL-2B-1-5InternVL2-2B
Model Size2.9B2.2B2.2B
DocVQAtest-85.086.9
ChartQAtest-74.876.2
InfoVQAtest-55.458.9
TextVQAval68.170.573.4
OCRBench614654

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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