WeVisOne-VL-2B：新機能、必要なハードウェア、無料での動かし方

Tencent released WeVisOne-VL-2B, a 2.4 billion-parameter vision-language model that handles objects and pixels in one go. It runs on a modest RTX 4060 with just 4 GB memory.

WeVisOne-VL-2B is a vision-language model from Tencent's WeChat Vision team. According to the model card, it builds on Qwen3-VL and adds a lightweight 9.5 million-parameter pixel decoder. The model can predict bounding boxes, points, and full-resolution alpha mattes directly from images, using special coordinate and instance tokens.

The new part is a dedicated pixel decoder that combines a visual feature adapter, a box-guided query decoder, and an instance-conditioned dense prediction head. It produces dense outputs like segmentation and matting without a separate processing stage. The model also returns None when a queried target is absent, simplifying downstream handling.

According to the model card, the model excels at open-vocabulary object detection, referring localization, counting, OCR, segmentation, and promptable alpha matting. However, fine alpha boundaries can be affected by image resolution, severe occlusion, complex transparency, and how prompts are phrased. It is designed for single-turn image perception; video grids are rejected.

The release is aimed at research on visual grounding, referring localization, pointing, counting, OCR localization, segmentation, and image matting. Because it fits in the memory of an RTX 4060 at 4 GB, hobbyists with a modest GPU can also experiment. No free API is available yet, but the weights can be downloaded from Hugging Face.

自宅で動かすには何が必要でしょうか。4ビットでは約4ギガバイトのメモリを使います。完全に収まる最小の家庭用マシンはRTX 4060です。RTX 3060 12 GBとRAM 32ギガバイトでは、毎秒約105トークンで書きます。RTX 4090とRAM 64ギガバイトでは、毎秒約247トークンで書きます。

まだ無料 API はありません。ウェイトはオープンで、Apache 2.0 ライセンスの下で Hugging Face からダウンロード可能です。Arena ではまだランキングされていません：投票が届き次第スコアを追加します。

Compare WeVisOne VL 2B with every free model, and check what your own GPU can run, on FreeLLMArena.