Meta SAM 3.1 Released: Faster Open-Source Video

Quick Answer: What is Meta SAM 3.1? Meta SAM 3.1 (Segment Anything Model 3.1) is a unified open-source foundation model for promptable segmentation in images and videos. The newly released SAM 3.1 update introduces object multiplexing—a shared-memory approach for joint multi-object tracking. This breakthrough doubles video processing speed (up to 32 FPS on an H100 GPU) without losing accuracy, making high-performance computer vision tasks and local AI inference far more accessible for developers using consumer hardware.

The Release of Meta SAM 3.1: A Leap in Video Segmentation

In late March 2026, Meta’s Superintelligence Labs released SAM 3.1, a powerful drop-in update to their groundbreaking Segment Anything Model 3. SAM 3 already revolutionized the field by introducing the ability to exhaustively segment all instances of an open-vocabulary concept (handling over 50x more unique concepts than previous benchmarks).

Now, SAM 3.1 focuses on solving one of the biggest bottlenecks in computer vision: video processing efficiency.

By sharing this update with the open-source community, Meta is actively pushing high-performance AI applications out of massive server farms and onto smaller, more accessible hardware.

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How Object Multiplexing Doubles Processing Speed

The core innovation in SAM 3.1 is Object Multiplexing.

In previous iterations, tracking multiple objects across a video frame-by-frame was computationally heavy. SAM 3.1 introduces a shared-memory approach for joint multi-object tracking.

The Performance Gains:

• Doubled Throughput: For videos with a medium number of objects, processing speed increases from 16 to 32 frames per second (FPS) on a single H100 GPU.
• High-Scale Efficiency: It delivers ~7x faster inference when tracking up to 128 objects simultaneously.
• Zero Accuracy Loss: Astonishingly, this speed does not come at the cost of precision. SAM 3.1 actually shows improved Video Object Segmentation (VOS) performance on 6 out of 7 industry benchmarks.

Why SAM 3.1 Matters for Local-First AI

At Vucense, we track how AI models impact compute sovereignty. The sheer size of early foundation models forced developers to rely on cloud APIs, creating massive privacy risks.

SAM 3.1 is a vital step toward Local AI video segmentation. By drastically reducing the computational overhead required to track multiple objects in real-time, Meta is making it feasible to run these models on smaller, local rigs.

Key Use Cases for Local Video Segmentation:

1. Privacy-First Home Security: Process local security camera feeds without sending video to the cloud. By running SAM 3.1 on a local home server, you can set up alerts for specific open-vocabulary events (e.g., “a person carrying a package” or “an unrecognized dog in the yard”) while ensuring your property’s video stream remains entirely within your physical walls.
2. Autonomous Robotics & Edge Computing: Enable edge-devices to track objects in real-time without latency-heavy API calls. For example, a local drone or a warehouse robot can use SAM 3.1 to track up to 128 dynamic obstacles simultaneously (like workers, forklifts, and pallets) using the new object multiplexing feature, ensuring safe navigation even in internet-dead zones.
3. Local Video Editing & Rotoscoping: Build advanced rotoscoping tools directly into desktop applications. Content creators can feed a video into SAM 3.1, prompt it with “the main subject wearing sunglasses,” and the model will generate pixel-perfect alpha mattes for that subject across every frame at 32 FPS, completely replacing tedious manual masking.
4. Medical Imaging & Diagnostics: In sovereign healthcare environments where patient data privacy is paramount, hospitals can deploy SAM 3.1 on local workstations. Doctors can use text prompts like “highlight the tumor” or “segment the left ventricle” across 3D MRI video slices, getting immediate visual isolation without exposing protected health information (PHI) to external cloud servers.

Whether you are building privacy-first home security systems, autonomous robotics, or local video editing tools, learning how to use Meta SAM 3.1 locally lowers the hardware barrier to entry.

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How to Get Started with SAM 3.1: A Developer’s Guide

For developers looking to integrate SAM 3.1 into their local sovereign stacks, the setup process is straightforward but requires specific hardware prerequisites. The model is fully open-source, though you must request checkpoint access via Hugging Face.

Hardware & Software Prerequisites

To run SAM 3.1 locally, ensure your system meets the following requirements:

• Python: Version 3.12 or higher
• PyTorch: Version 2.7 or higher
• GPU: CUDA-compatible GPU with CUDA 12.6 or higher (an H100 is ideal for max throughput, but smaller local GPUs can run it with reduced frame rates).

Step 1: Environment Setup

First, create an isolated Conda environment to avoid dependency conflicts:

conda create -n sam3 python=3.12
conda deactivate
conda activate sam3

Install PyTorch with CUDA 12.8 support (adjust according to your specific CUDA version):

pip install torch==2.10.0 torchvision --index-url https://download.pytorch.org/whl/cu128

Step 2: Install the SAM 3.1 Codebase

You need to clone the official Meta repository and install it. If you previously installed SAM 3, you must run git pull to fetch the new object multiplexing updates before reinstalling.

git clone https://github.com/facebookresearch/sam3.git
cd sam3
pip install -e .

Install additional dependencies for notebooks or development:

# For running example notebooks
pip install -e ".[notebooks]"

# For development and training
pip install -e ".[train,dev]"

Optional but highly recommended for faster inference: Install flash-attn and cc_torch:

pip install einops ninja && pip install flash-attn-3 --no-deps --index-url https://download.pytorch.org/whl/cu128
pip install git+https://github.com/ronghanghu/cc_torch.git

Step 3: Authenticate and Download Checkpoints

Before using SAM 3, you must request access on the Hugging Face SAM 3 repository. Once accepted, you need to be authenticated to download the checkpoints. Run the following command after generating an access token:

hf auth login

Step 4: Running Basic Image Inference (Python)

Here is a basic example of how to load the model and run an open-vocabulary text prompt on a local image:

import torch
from PIL import Image
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor

# 1. Load the SAM 3.1 model and processor
model = build_sam3_image_model()
processor = Sam3Processor(model)

# 2. Load your local image
image = Image.open("<YOUR_IMAGE_PATH.jpg>")
inference_state = processor.set_image(image)

# 3. Prompt the model with open-vocabulary text
output = processor.set_text_prompt(
    state=inference_state, 
    prompt="<YOUR_TEXT_PROMPT>"
)

# 4. Extract the results
masks, boxes, scores = output["masks"], output["boxes"], output["scores"]

print(f"Detected {len(masks)} objects matching the prompt.")

Step 5: Running Video Inference (The Object Multiplexing Advantage)

Where SAM 3.1 truly shines is video processing. The new build_sam3_video_predictor module uses the shared-memory object multiplexing to handle multiple frames efficiently. Here is how to initiate a video session:

from sam3.model_builder import build_sam3_video_predictor

# 1. Initialize the video predictor
video_predictor = build_sam3_video_predictor()
video_path = "<YOUR_VIDEO_PATH>" # Can be an MP4 file or a folder of JPEGs

# 2. Start a video session
response = video_predictor.handle_request(
    request=dict(
        type="start_session",
        resource_path=video_path,
    )
)

session_id = response["session_id"]

# 3. Add a prompt to a specific frame (e.g., frame 0)
response = video_predictor.handle_request(
    request=dict(
        type="add_prompt",
        session_id=session_id,
        frame_index=0,
        prompt="<YOUR_TEXT_PROMPT_OR_MASK>",
    )
)

print("Video session initiated and prompt added successfully.")

Official Jupyter Notebook Examples

For hands-on learning, Meta provides several official Jupyter Notebooks in the notebooks/ directory of the GitHub repository. These are highly recommended for developers:

• image_predictor_example.ipynb: Demonstrates image segmentation using various prompt types (points, boxes, masks, and text).
• video_predictor_example.ipynb: A comprehensive guide to using SAM 3.1 on videos, highlighting the new object multiplexing capabilities for multi-object tracking.
• automatic_mask_generator_example.ipynb: Shows how to use the model to automatically segment everything in an image without specific prompts.

To run these notebooks locally, ensure you installed the optional notebook dependencies during Step 2 (pip install -e ".[notebooks]").

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Frequently Asked Questions (FAQ)

What is Meta SAM 3.1? SAM 3.1 (Segment Anything Model 3.1) is an open-source foundation model by Meta used for detecting, segmenting, and tracking objects in images and videos using text or visual prompts.

What makes SAM 3.1 better than SAM 3? SAM 3.1 introduces “object multiplexing,” a shared-memory approach that doubles video processing speeds (from 16 to 32 FPS) and offers up to 7x faster inference when tracking a massive number of objects (like 128 objects), all without sacrificing accuracy.

Can I run SAM 3.1 locally? Yes. SAM 3.1 is an open-source model available on GitHub and Hugging Face. Because of its new object multiplexing efficiencies, it requires less computational power than previous versions, making it highly suitable for local-first AI applications on capable hardware.

Sources & Further Reading

• MIT Technology Review — AI Section — In-depth coverage of AI research and industry trends
• arXiv AI Papers — Pre-print research papers on AI and machine learning
• EFF on AI — Civil liberties perspective on AI policy