---
license: other
tags:
- heal
- horizon
---

# InternVL2-2B

**Original model repository:** [OpenGVLab/InternVL2-2B](https://huggingface.co/OpenGVLab/InternVL2-2B)

## Model Introduction

InternVL2-2B is an instruction-tuned Vision-Language Model (VLM) for image understanding and text generation. It combines an InternViT-300M vision encoder, an MLP projector, and InternLM2-Chat-1.8B as its language model. Typical applications include visual question answering, OCR, image description, document and chart understanding, and multimodal dialogue.

## Deployment Metrics

### Model Parameters

| Metric | Value |
|---|---:|
| Total model parameters | 2.206B |
| Vision model (ViT) parameters | 316.6M |
| Language model (LM) parameters | 1.889B |

Parameter counts are calculated from the tensors stored in the upstream checkpoint.

### Performance Metrics

| Chips | Data Type | ViT Image Size | Sequence Length (tokens) | Maximum Context Length (tokens) | BPU Cores (ViT / Prefill / Decode) | ViT Latency (ms) | TTFT (ms) | Prefill TPS (token/s) | Decode TPS (token/s) | BPU Memory (GB) | CPU Memory (GB) |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| J6P | W8A8 | 448 × 448 | 512 | 1024 | 4 / 4 / 4 | 41.535 | 84.448 | 13,265.273 | 70.473 | 2.4 | 0.79 |

> **Note:** TTFT includes preprocessing and ViT latency. Memory values represent the peak memory usage measured during the specified performance test.