Qwen3-VL-2B-Instruct

Original model repository: Qwen/Qwen3-VL-2B-Instruct

Model Introduction

Qwen3-VL-2B-Instruct is an instruction-tuned Vision-Language Model (VLM) for understanding images, videos, and text. It combines a vision encoder with a dense autoregressive language model. The model supports visual question answering, multilingual OCR, document understanding, visual grounding, spatial reasoning, video understanding, visual coding, and visual-agent tasks.

Deployment Metrics

Model Parameters

Metric	Value
Total model parameters	2.128B
Vision model (ViT) parameters	407.0M
Language model (LM) parameters	1.721B

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

Performance Metrics

Chips	Scenario	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)
Matrix6P	Image	W8A8	448 × 448	512	1024	4 / 4 / 4	18.489	76.787	9,567.264	61.967	3.0	1.3
S6P	Image	W8A8	448 × 448	512	1024	4 / 4 / 4	16.662	68.262	10,786.589	67.446	3.1	1.3
S6P	Video	W8A8	448 × 448 per frame	1024	4096	4 / 4 / 4	80.971	452.232	7,462.987	43.671	4.9	1.4

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