Run SpatialLM on macos: Step by Step Guide

SpatialLM is a cutting-edge 3D large language model that enables advanced spatial understanding through multimodal processing of point clouds, video inputs, and sensor data. While primarily designed for CUDA-enabled systems.

This guide provides detailed instructions for running SpatialLM on macOS using optimized workflows and hardware configurations.

System Requirements for macOS

Minimum Specifications:

• Apple Silicon (M1/M2/M3) or Intel Core i7/i9 processor
• 16GB unified memory (32GB recommended)
• macOS Ventura 13.0 or newer
• 20GB free storage space

Recommended Setup:

• M2 Ultra with 64GB RAM
• 1TB SSD storage
• Python 3.11 via Miniforge
• PyTorch 2.4.1 with Metal Performance Shaders (MPS)

Installation Process

1. Environment Setup

# Install Miniforge for Apple Silicon
curl -L https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh -o Miniforge.sh
bash Miniforge.sh

# Create a dedicated environment
conda create -n spatiallm python=3.11
conda activate spatiallm

2. Dependency Installation

# Install base packages
conda install -y numpy scipy libpython mkl mkl-include

# Install PyTorch with MPS support
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

# Install SpatialLM requirements
git clone https://github.com/manycore-research/SpatialLM.git
cd SpatialLM
pip install poetry
poetry install --no-cuda  # Disable CUDA dependencies

3. Model Download

Download the macOS-optimized Qwen-0.5B model:

wget https://huggingface.co/manycore/SpatialLM-Qwen-0.5B/resolve/main/spatiallm_qwen_0.5b_macos.pt

Configuration Tuning for Apple Silicon

Create macos_config.yaml:

hardware:
  device: mps  # Use Metal Performance Shaders
  precision: mixed16  # Mixed precision training
  memory_optimization: true

model:
  architecture: qwen-0.5b
  checkpoint: ./spatiallm_qwen_0.5b_macos.pt
  quantization: 4-bit  # Reduces memory usage by 75%

processing:
  pointcloud:
    max_points: 250000  # Reduced for MPS compatibility
  video:
    max_resolution: 720p

Running Inference

Basic Point Cloud Processing

from spatiallm import SpatialLMProcessor

processor = SpatialLMProcessor(config="macos_config.yaml")
results = processor.process(
    input_path="living_room.mp4",
    output_formats=["3d_layout", "2d_floorplan"],
    visualization=True
)

print(f"Detected {len(results['walls'])} walls")
print(f"Identified {len(results['furniture'])} furniture pieces")

Performance Benchmarks

Advanced Workflow Optimization

1. Memory Management Strategies

• Enable swap compression with:

sudo nvram boot-args="vm_compressor=1"

• Configure unified memory allocation:

export PYTORCH_MPS_MEMORY_LIMIT=0.8  # Limit to 80% of total RAM

2. Real-time Processing Pipeline

import cv2
from spatiallm.realtime import SpatialLMStream

stream = SpatialLMStream(
    camera_index=0,
    processing_resolution=(1280, 720),
    update_interval=5  # Update model every 5 seconds
)

while True:
    frame, analysis = stream.get_frame()
    cv2.imshow('SpatialLM Analysis', frame)
    if cv2.waitKey(1) == ord('q'):
        break

stream.release()

Troubleshooting Common Issues

Problem: Model Initialization Failures

Solution:

# Reset Metal shader cache
sudo rm -rf /private/var/folders/*/*/*/com.apple.Metal*

Problem: Memory Allocation Errors

Solution:

# Enable memory-efficient attention
from spatiallm.utils import enable_mem_efficient_attention
enable_mem_efficient_attention()

Professional Applications on macOS

Architectural Workflow Integration

1. Record space with iPhone LiDAR
2. Process through SpatialLM
3. Export to AutoCAD/Rhino formats
4. Generate material estimates

graph TD
    A[iPhone LiDAR Scan] --> B(SpatialLM Processing)
    B --> C{Output Format}
    C --> D[CAD File]
    C --> E[3D Model]
    C --> F[Material List]

Robotics Development

• Implement spatial awareness in Swift robotics frameworks
• Create AR navigation systems using RealityKit
• Develop home automation controllers

Alternative Deployment Options

For resource-intensive tasks, consider cloud integration:

# Configure hybrid processing
spatiallm-cli --local-device mps --cloud-backend aws --batch-size 4

This setup offloads complex computations to cloud GPUs while maintaining real-time responsiveness through local processing.

Maintenance and Updates

Recommended Update Schedule

• Weekly model checkpoint updates
• Monthly framework upgrades
• Quarterly full system validation

# Update workflow
conda activate spatiallm
pip install --upgrade spatiallm
spatiallm-update-models --channel stable

By following this comprehensive guide, macOS users can effectively utilize SpatialLM for professional 3D spatial analysis while maintaining system stability and performance. The combination of Apple's Metal acceleration and SpatialLM's optimized models enables complex spatial reasoning tasks on local hardware, opening new possibilities for developers and researchers in the Apple ecosystem.

References

1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
3. Run DeepSeek Janus-Pro 7B on Windows: A Complete Installation Guide