SenseNova-Vision：一个7B模型做完所有CV任务，需要什么硬件？

SenseNova-Vision: Unified Multimodal CV Model — Hardware Guide for Every Use Case

Research #计算机视觉#多模态#AI模型#GPU#硬件#深度估计#目标检测#分割#开源
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仓库：OpenSenseNova/SenseNova-Vision · Python · Apache 2.0
论文：arXiv 2607.06560 “Vision as Unified Multimodal Generation”
模型：sensenova/SenseNova-Vision-7B-MoT（HuggingFace）
数据集：SenseNova-Vision-Corpus-50M（5000万样本）

一、一个模型，所有计算机视觉任务

SenseNova-Vision 的出发点很直接：把所有计算机视觉任务统一成一个生成问题。

不管是目标检测、深度估计、图像分割、OCR、关键点检测还是多视角3D重建，都通过文本或图像的生成形式来表达。

具体说：

输出类型覆盖任务
文本生成目标检测框（坐标）、OCR文字、GUI grounding、关键点坐标、相机参数
图像生成深度图、法向量图、分割掩码、多视角点图
混合输出GCG（Grounded Caption Generation）等组合任务

模型名字里的 MoT = Mixture-of-Tasks，是用同一套架构在5000万个多任务样本上训练出来的统一模型，参数量7B。

二、硬件需求：不同使用场景的配置要求

这是用户最关心的问题。不同使用模式对硬件的要求差别很大。

场景 1：单图推理（本地测试）

export MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT/
bash scripts/run_sensenova_vision.sh inference depth "" examples/images/3.jpg

硬件要求：

  • 官方未明确标注最低显存
  • 7B模型 + flash-attn，实际加载约需 16-20GB VRAM（bfloat16精度）
  • 消费级显卡（RTX 4090 / 3090 / A5000）可试跑单图推理
  • CUDA 12.4 + PyTorch 2.5.1 是验证过的组合；其他 CUDA 12.x 版本可能可用

场景 2：Web Demo（Gradio 界面）

MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT \
  bash scripts/run_sensenova_vision.sh demo

官方推荐：1×80GB GPU（A100 80GB 或 H100 80GB）

全功能 Web Demo 需要同时支持文本生成、图像生成、多视角重建等所有任务模式，80GB 显存是稳定跑完所有任务的安全线。

场景 3：完整 Benchmark 推理

bash scripts/run_sensenova_vision.sh benchmark
# 内部调用：--num_gpus 8 --tasks_per_gpu 2

官方要求：至少 1 台 8×80GB GPU 机器

也就是：8张 A100 80GB（640GB 总显存）或等效配置。

8卡并行推理的原因是 benchmark 覆盖了全部任务类型的所有测试集，数据量大，单卡效率太低，多卡分任务跑可以在合理时间内完成。

场景 4：训练

最低：2台 8×80GB GPU 机器（128张80GB卡）

推荐：32台+ 同配置机器（256张80GB卡以上）

训练数据集 SenseNova-Vision-Corpus-50M 有5000万个样本，覆盖所有任务类别。这个规模的训练需要大规模分布式计算。

三、硬件需求速查表

使用场景最低配置是否可行
单图推理RTX 4090 (24GB) 或更高✅ 可试跑
交互式推理（保持模型加载）RTX 4090 (24GB)+✅ 可运行
Web Demo 全功能1×A100 80GB（推荐）需要80GB卡
完整 Benchmark1台 8×80GB 机器需要整台8卡服务器
模型训练（最小可用）2台 8×80GB 机器需要16张80GB卡
模型训练（推荐）32台+ 8×80GB 机器大规模分布式集群

四、环境依赖

# 验证过的组合
PyTorch 2.5.1 + cu124（CUDA 12.4）
flash-attn 2.6.3

# 其他依赖
decord, fastevaluate, panopticapi, torch

验证安装：

python -c 'import decord, fastevaluate, flash_attn, panopticapi, torch; \
  print("torch=%s cuda=%s flash_attn=%s" % (torch.__version__, torch.version.cuda, flash_attn.__version__))'

官方说其他 CUDA 12.x 版本”可能可用”，但 flash-attn 版本需要和 Python/PyTorch/CUDA 版本匹配。

五、快速上手

git clone https://github.com/OpenSenseNova/SenseNova-Vision.git
cd SenseNova-Vision
bash setup.sh sensenova-vision
conda activate sensenova-vision
# 下载模型到本地
export MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT/
# 跑内置示例（深度、分割、检测等）
bash scripts/run_sensenova_vision.sh example

九个内置示例覆盖：通用理解、二值分割、深度估计、法向量估计、GCG 分割、目标检测、多视角3D重建、Panoptic 分割、交互式分割。

六、支持的任务列表

任务类型命令输出格式
通用问答raw_query文本
深度估计depth深度图(.png)
法向量估计normal法向量图(.png)
二值分割binary_seg掩码 + 可视化
Panoptic 分割pan_seg掩码 + 可视化
GCG 分割gcg_seg掩码 + caption
目标检测bbox_detection检测框坐标(txt) + 可视化
关键点检测keypoint关键点坐标 + 可视化
OCRocr文字内容(txt)
多视角3D重建recon3d点图(.npy) + 3D场景(.glb)
相机姿态估计camera_pose相机参数(json)

七、判断

从硬件需求的角度来看，SenseNova-Vision 是一个研究导向的系统。

消费级配置（24GB显卡）可以跑单图推理，适合验证模型能力。但要体验完整的 Demo 或跑 benchmark，就需要进入数据中心级硬件的范畴——80GB 显卡是起点，完整 benchmark 需要8卡服务器。

这个权衡是合理的：把11个计算机视觉任务统一进一个7B模型，而不是为每个任务维护一个专门的模型，系统复杂度大幅降低。代价是推理硬件要求相对偏高——但对于需要多任务 CV 能力的团队，这个代价划算。

benchmark 结果显示：在几乎所有任务上，SenseNova-Vision 与同类专门模型（Grounding DINO、DepthAnything、PSALM、DUSt3R）持平或更好。用一个模型做到这一点，是值得关注的。

数据来源：GitHub OpenSenseNova/SenseNova-Vision，arXiv 2607.06560，2026-07-23 采集。

© 2026 Author: Mycelium Protocol

🇬🇧 English

Repository: OpenSenseNova/SenseNova-Vision · Python · Apache 2.0
Paper: arXiv 2607.06560 “Vision as Unified Multimodal Generation”
Model: sensenova/SenseNova-Vision-7B-MoT (HuggingFace)
Dataset: SenseNova-Vision-Corpus-50M (50 million samples)

1. One Model, All Computer Vision Tasks

SenseNova-Vision’s premise is straightforward: unify all computer vision tasks into a single generative problem.

Whether it’s object detection, depth estimation, image segmentation, OCR, keypoint detection, or multi-view 3D reconstruction — everything is expressed through text or image generation.

Specifically:

Output TypeTasks Covered
Text generationObject detection boxes (coordinates), OCR text, GUI grounding, keypoint coordinates, camera parameters
Image generationDepth maps, normal maps, segmentation masks, multi-view point maps
Mixed outputGCG (Grounded Caption Generation) and other combined tasks

The MoT in the model name stands for Mixture-of-Tasks — a unified model trained on 50 million multi-task samples using the same architecture, with 7B parameters.

2. Hardware Requirements: Configuration for Different Use Cases

This is the question users care about most. Hardware requirements vary significantly across different usage modes.

Scenario 1: Single-Image Inference (Local Testing)

export MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT/
bash scripts/run_sensenova_vision.sh inference depth "" examples/images/3.jpg

Hardware requirements:

  • Official documentation does not specify a minimum VRAM requirement
  • A 7B model with flash-attn requires approximately 16–20 GB VRAM to load (bfloat16 precision)
  • Consumer GPUs (RTX 4090 / 3090 / A5000) can attempt single-image inference
  • CUDA 12.4 + PyTorch 2.5.1 is the verified combination; other CUDA 12.x versions may also work

Scenario 2: Web Demo (Gradio Interface)

MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT \
  bash scripts/run_sensenova_vision.sh demo

Official recommendation: 1×80 GB GPU (A100 80 GB or H100 80 GB)

The full-featured Web Demo must simultaneously support text generation, image generation, multi-view reconstruction, and all other task modes. 80 GB VRAM is the safe threshold for stable execution across all task types.

Scenario 3: Full Benchmark Inference

bash scripts/run_sensenova_vision.sh benchmark
# Internally calls: --num_gpus 8 --tasks_per_gpu 2

Official requirement: At least 1 machine with 8×80 GB GPUs

That is: 8× A100 80 GB (640 GB total VRAM) or equivalent.

The reason for 8-GPU parallel inference is that the benchmark covers all task types across all test sets. The data volume is large, and single-GPU efficiency is too low — distributing tasks across multiple GPUs enables completion within a reasonable timeframe.

Scenario 4: Training

Minimum: 2 machines with 8×80 GB GPUs each (16× 80 GB total)

Recommended: 32+ machines with the same configuration (256+ × 80 GB GPUs)

The training dataset SenseNova-Vision-Corpus-50M contains 50 million samples covering all task categories. Training at this scale requires large-scale distributed computation.

3. Hardware Quick-Reference Table

Use CaseMinimum ConfigurationFeasibility
Single-image inferenceRTX 4090 (24 GB) or higher✅ Can attempt
Interactive inference (model kept loaded)RTX 4090 (24 GB)+✅ Runnable
Full-featured Web Demo1× A100 80 GB (recommended)Requires 80 GB GPU
Full Benchmark1 machine with 8×80 GB GPUsRequires a full 8-GPU server
Model training (minimum viable)2 machines with 8×80 GB GPUsRequires 16× 80 GB GPUs
Model training (recommended)32+ machines with 8×80 GB GPUsLarge-scale distributed cluster

4. Environment Dependencies

# Verified combination
PyTorch 2.5.1 + cu124 (CUDA 12.4)
flash-attn 2.6.3

# Other dependencies
decord, fastevaluate, panopticapi, torch

Verify installation:

python -c 'import decord, fastevaluate, flash_attn, panopticapi, torch; \
  print("torch=%s cuda=%s flash_attn=%s" % (torch.__version__, torch.version.cuda, flash_attn.__version__))'

The official documentation states that other CUDA 12.x versions “may work,” but the flash-attn version must be compatible with your specific Python / PyTorch / CUDA combination.

5. Quick Start

git clone https://github.com/OpenSenseNova/SenseNova-Vision.git
cd SenseNova-Vision
bash setup.sh sensenova-vision
conda activate sensenova-vision
# Download model locally
export MODEL_PATH=/path/to/SenseNova-Vision-7B-MoT/
# Run built-in examples (depth, segmentation, detection, etc.)
bash scripts/run_sensenova_vision.sh example

Nine built-in examples cover: general understanding, binary segmentation, depth estimation, normal estimation, GCG segmentation, object detection, multi-view 3D reconstruction, panoptic segmentation, and interactive segmentation.

6. Supported Task List

Task TypeCommandOutput Format
General Q&Araw_queryText
Depth estimationdepthDepth map (.png)
Normal estimationnormalNormal map (.png)
Binary segmentationbinary_segMask + visualization
Panoptic segmentationpan_segMask + visualization
GCG segmentationgcg_segMask + caption
Object detectionbbox_detectionDetection box coordinates (.txt) + visualization
Keypoint detectionkeypointKeypoint coordinates + visualization
OCRocrText content (.txt)
Multi-view 3D reconstructionrecon3dPoint map (.npy) + 3D scene (.glb)
Camera pose estimationcamera_poseCamera parameters (.json)

7. Assessment

From a hardware requirements perspective, SenseNova-Vision is a research-oriented system.

Consumer-grade configurations (24 GB GPU) can run single-image inference, making them suitable for validating model capabilities. But to experience the full Demo or run benchmarks, you enter the realm of data center hardware — an 80 GB GPU is the entry point, and a full benchmark requires an 8-GPU server.

This trade-off is reasonable: consolidating 11 computer vision tasks into a single 7B model — rather than maintaining a specialized model for each task — dramatically reduces system complexity. The cost is somewhat higher inference hardware requirements — but for teams that need multi-task CV capabilities, the cost is worthwhile.

Benchmark results show that SenseNova-Vision matches or outperforms comparable specialized models (Grounding DINO, DepthAnything, PSALM, DUSt3R) on almost all tasks. Achieving this with a single model is worth noting.

Data source: GitHub OpenSenseNova/SenseNova-Vision, arXiv 2607.06560, collected 2026-07-23.

© 2026 Author: Mycelium Protocol

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