Orchard AI Models: High-Performance DePIN Batch

The Orchard network provides massive-scale, decentralized AI batch powered exclusively by our distributed Apple Silicon fleet. By shifting compute from centralized, industrial data centers to a global mesh of Mac hardware, we deliver frontier-level intelligence with zero water consumption, zero noise pollution, and a radically reduced carbon footprint.

Our model library is specifically optimized for Apple's Unified Memory Architecture (UMA), allowing us to offer large parameter models with unmatched cost-efficiency for batch extraction, structured data processing, and 3D asset generation.

I. Text & Reasoning Models

Our text models are engineered to handle massive batch workloads—processing everything from localized JSON extraction to complex multi-document reasoning at unprecedented speeds.

• Llama 3.2 3B (Standard): The workhorse of the network. Optimized for extreme low-latency processing, this model is ideal for rapid classification, sentiment analysis, and straightforward data structuring.
• Qwen 3.7 Max MoE (High Efficiency): Our premier Mixture-of-Experts architecture. While it contains the vast intelligence of a 35-billion parameter foundation model, it activates only a fraction of those parameters per token. This provides enterprise-grade reasoning and logic capabilities at the processing speed—and price point—of a much smaller model.
• Qwen 3.8 27B (Heavy Compute): Built for the most demanding extraction and reasoning tasks. Thanks to our high-RAM network nodes (32GB+ to 352GB Exo clusters), this dense model runs natively in unified memory with massive context windows, eliminating the PCIe bandwidth bottlenecks that cripple traditional PC setups.

II. Vision & Spatial Models

Orchard's multi-modal capabilities allow buyers to process unstructured visual data, extracting precise text, bounding boxes, and spatial context from dense imagery.

• Qwen 2.5-VL 3B: A lightning-fast vision model perfect for standard OCR tasks, receipt scanning, and basic image tagging.
• Qwen 3-VL 8B: Our core production vision engine. It seamlessly balances high-resolution spatial understanding with rapid throughput, making it the standard choice for complex document ingestion (charts, graphs, and UI analysis).
• Qwen 3-VL 32B (BETA): Our flagship multi-modal powerhouse. Currently in beta for heavy network nodes, this model delivers frontier-level spatial intelligence and deep image comprehension for highly complex visual reasoning tasks.

III. 3D Asset Generation Models

Our decentralized compute grid powers the next generation of spatial intelligence and object creation, transforming 2D inputs into rich, production-ready 3D models.

• Trellis 3D: A state-of-the-art 3D asset generation system. Trellis synthesizes high-quality 3D objects from a single text or image prompt in just a few seconds. Utilizing a novel Structured LATent (SLAT) representation, this model captures rich geometric shapes and surface textures. It uniquely decodes into multiple output formats simultaneously—including meshes (GLB), 3D Gaussian Splatting, and Radiance Fields (NeRF)—making it a highly versatile building block for game engines, AR/VR, and e-commerce.

IV. The Orchard Green Edge: A New Paradigm for AI Compute

Traditional AI hyperscalers rely on massive industrial data centers that extract a heavy toll on local environments. Orchard flips this model entirely. Our "data centers" are the Mac Minis and Mac Studios already sitting in living rooms, dorms, and offices around the world.

Zero Water Consumption Traditional AI inference is incredibly water-intensive; a single training run for large models can consume 750 million liters of water for cooling alone. Standard data center cooling systems reject heat by evaporating roughly 1.8 gallons of water per ton-hour of cooling. Orchard uses exactly zero gallons of water for cooling. Our distributed Apple Silicon nodes rely entirely on ambient room temperature and hyper-efficient passive/active air cooling built into consumer hardware.

Ultra-Low Carbon Footprint Because the GPU and CPU share a single physical memory pool, Apple Silicon eliminates the massive power drain of pushing data across traditional PCIe or NVLink buses. During sustained inference, an M4 Pro chip draws only 0.47 watts for the CPU/GPU package, with a total system power draw of just 8 to 12 watts. This delivers a 30x to 40x better energy efficiency per token compared to traditional data center GPUs, radically lowering the carbon emissions required to process your batch workloads.

Zero Noise Pollution Hyperscale data centers generate relentless, industrial-grade noise pollution that disrupts local communities. Orchard's network is virtually silent. By utilizing consumer Apple hardware, our nodes execute heavy workloads while producing minimal thermal output and zero localized noise pollution, making our network completely invisible to the communities where our compute is generated.