orchard-llm 0.1.1

High-performance LLM runtime for Apple Silicon

Orchard

The High-Performance LLM Runtime for Apple Silicon.

Orchard is a specialized inference engine designed to extract maximum performance from Apple Silicon (M1/M2/M3/M4) chips. By bypassing generic frameworks and targeting the Metal API directly, Orchard achieves state-of-the-art speed and efficiency for local Large Language Model (LLM) inference.

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Key Features

• Apple Silicon Native: Built directly on Metal (Objective-C++) for zero-overhead GPU access. No PyTorch, no TensorFlow, just raw compute.
• 4-bit Quantization: Custom INT4 kernels allow running massive models on consumer hardware (e.g., 7B models on 8GB RAM).
• Blazing Fast: Up to 82x faster than CPU inference for quantized workloads.
• Zero-Copy Architecture: Unified memory architecture is fully exploited—CPU and GPU share memory pointers where possible.
• Pythonic Control: A lightweight, high-level Python API controls the heavy lifting done in C++.

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Installation

Prerequisites

• macOS 13.0+ (Ventura or later)
• Apple Silicon (M1/M2/M3/M4)
• Python 3.9+
• Xcode Command Line Tools (xcode-select --install)

Quick Install (From Source)

# 1. Clone the repository
git clone https://github.com/tarso-bertolini/Orchard.git
cd Orchard

# 2. Create a virtual environment (Recommended)
python3 -m venv .venv
source .venv/bin/activate

# 3. Install
pip install .

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CLI Usage

Orchard comes with a powerful CLI to manage your local AI environment.

1. Check System Compatibility

See what your hardware can handle.

orchard info

2. Download Models

Interactive downloader for popular models (Llama-2, Mistral, TinyLlama).

orchard download

3. Optimize Models (New!)

Pre-quantize models to 4-bit format for instant loading and reduced disk usage.

orchard optimize --model models/Llama-2-7b-chat --output models/Llama-2-7b-chat-opt

4. Run Inference

Chat with your model immediately.

orchard run --model models/Llama-2-7b-chat-opt --prompt "Explain quantum computing like I'm 5."

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Python API

Integrate Orchard into your own applications with just a few lines of code.

from orchard import Llama

# Load a model (supports Hugging Face format)
# If the model is not optimized, it will be quantized on-the-fly.
model = Llama("models/Llama-2-7b-chat-opt")

# Generate text (Batched Inference)
print("Generating...")
prompts = [
    "Write a haiku about apples.",
    "Explain quantum computing in one sentence."
]

results = model.generate_batch(
    prompts=prompts,
    max_tokens=50,
    temperature=0.7
)

for prompt, result in zip(prompts, results):
    print(f"Prompt: {prompt}")
    print(f"Result: {result}\n")

# Load LoRA Adapter (New!)
# Apply a fine-tuned adapter on top of the base model
model.load_lora("adapters/alpaca-lora-7b")

print("Generating with LoRA...")
results = model.generate_batch(["Tell me a story about a llama."])
print(results[0])

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Performance Benchmarks

Device: Apple M2 (Unified Memory)

Operation	Implementation	Time	Speedup
Matrix Mul (FP32)	NumPy (CPU)	15.15 ms	1.0x
Matrix Mul (FP32)	Metal (GPU)	26.82 ms	0.6x (Overhead bound)
Llama Layer (INT4)	NumPy (FP16)	34.58 ms	1.0x
Llama Layer (INT4)	Orchard (Metal)	0.42 ms	82.3x

See BENCHMARKS.md for the full report.

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Architecture

Orchard uses a hybrid execution model to balance flexibility and performance.

graph TD
    User[User Python Script] --> API[Orchard Python API]
    API --> Loader[Model Loader & Quantizer]
    API --> Bindings[PyBind11 Interface]
    
    subgraph "Orchard Core (C++)"
        Bindings --> Backend[Metal Backend]
        Backend --> Tensor[Tensor Runtime]
    end
    
    subgraph "Metal GPU"
        Tensor --> Kernels[Custom Metal Shaders]
        Kernels --> MatMul[SIMD MatMul]
        Kernels --> RMS[RMSNorm]
        Kernels --> RoPE[Rotary Embeddings]
        Kernels --> Softmax[Softmax]
    end

Memory Model

• Weights: Stored in VRAM (GPU) as packed INT4 textures.
• KV Cache: Managed in RAM (CPU) and streamed to GPU on-demand (Hybrid Strategy for v0.1).
• Activations: Transient GPU buffers.

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Roadmap

• Phase 1: Metal Viability (Kernels for MatMul, Softmax, RMSNorm)
• Phase 2: Runtime Core (C++ Backend, Tensor Abstraction)
• Phase 3: Packaging (CLI, PyPI structure, Offline Optimization)
• Phase 4: Advanced Features (Batched Inference, Continuous Batching)
• Phase 5: LoRA Support (Runtime adapter composition)

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Contributing

Orchard is an open exploration of high-performance computing on Apple Silicon. Contributions, issues, and PRs are welcome!

1. Fork the repo.
2. Create your feature branch (git checkout -b feature/amazing-feature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request.

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License

MIT License. See LICENSE for details.

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