InternVL2-2B

Original model repository: 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.