OpenExplorer/InternVL3_5-1B

InternVL3.5-1B-Instruct

Original model repository: OpenGVLab/InternVL3_5-1B-Instruct

Model Introduction

InternVL3.5-1B-Instruct is an instruction-tuned Vision-Language Model (VLM) for multimodal perception and reasoning. It uses a vision encoder, an MLP projector, and an autoregressive language model to understand images and generate text. The model is designed for tasks such as OCR, document and chart understanding, visual question answering, multimodal reasoning, spatial understanding, and visual-agent applications.

Deployment Metrics

Model Parameters

Metric	Value
Total model parameters	1.061B
Vision model (ViT) parameters	309.3M
Language model (LM) parameters	751.6M

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

Performance Metrics

Test Configuration

Metric	Value
Platform	Matrix6P
Data type	W8A8
ViT image size	448 × 448
Sequence length	512
Maximum context length	1024
BPU cores (ViT / Prefill / Decode)	4 / 4 / 4

Performance Results

Metric	Value
ViT latency	28.201 ms
Time to first token (TTFT)	64.765 ms
Prefill throughput	15,652.844 tokens/s
Decode throughput	104.543 tokens/s

Memory Usage

Metric	Value
BPU memory	1.6 GB
CPU memory	0.76 GB

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