Apple Silicon GPU
Vendor: Apple
Category: GPU Graphics Processor
Architecture: Apple GPU (Unified Memory Architecture)
Introduction
Apple M-series chips (M1/M2/M3/M4/Pro/Max/Ultra) with built-in GPU, providing GPU accelerated computing through the Metal 3 framework. Unified memory architecture allows CPU and GPU to share memory, ideal for AI inference and machine learning.
Specifications
| Model | Compute | Memory | Interface | TDP | Process |
|---|---|---|---|---|---|
| M4 Ultra | 27 TFLOPS (FP16) | 256GB unified memory | Integrated SoC | 150W | N3E (3nm) |
| M3 Max | 18 TFLOPS (FP16) | 128GB unified memory | Integrated SoC | 100W | N3B (3nm) |
| M2 Ultra | 15 TFLOPS (FP16) | 192GB unified memory | Integrated SoC | 100W | N5 (5nm) |
Official Website
Driver Downloads
macOS
Linux
Related Documentation
- Metal Development Documentation
- Metal Performance Optimization Guide
- Apple Machine Learning Framework
- MLX (Apple ML Framework)
OS Support
| Windows | Linux | macOS | Android |
|---|---|---|---|
| ❌ | ⚠️ (Asahi Linux) | ✅ | ❌ |
Version History
| Version | Release Date | Description |
|---|---|---|
| Metal 3 | 2023 | M3 series mesh shading support |
| Core ML 7 | 2024 | M4 NPU deep integration |
Performance Benchmarks
| Model | Task | Performance Metric |
|---|---|---|
| M4 Ultra | Llama 3 70B Inference (MLX) | ~25 tok/s (quantized) |
| M3 Max | Llama 3 8B Inference | ~40 tok/s (4-bit) |
| M2 Ultra | Stable Diffusion XL | ~3.5s/img (batch=1) |
| M4 | Whisper-large-v3 | ~6× real-time transcription |
Pricing
| Model | Reference Price | Notes |
|---|---|---|
| M4 Ultra (Mac Pro/Studio) | ¥49,999 and up | Complete system purchase only |
| M3 Max (MacBook Pro) | ¥27,999 and up | Laptop form factor |
| M2 Ultra (Mac Studio) | ¥29,999 and up | Gradually being replaced by M4 |
Quick Installation
macOS (MLX Framework)
# 1. Install MLX (Apple official ML framework)
pip install mlx
# 2. Install MLX sample models
python -m mlx_lm.generate --model mlx-community/Llama-3.2-1B-4bit --max-tokens 50
macOS (llama.cpp MPS Backend)
# 1. Build llama.cpp (automatically enables Metal)
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make LLAMA_METAL=1
# 2. Run inference
./llama-cli -m models/llama-3.2-1b.Q4_K_M.gguf -p "Hello"
Code Examples
Python (MLX)
import mlx.core as mx
# Create tensor on Apple Silicon GPU
x = mx.random.normal((1024, 1024))
y = mx.matmul(x, x)
print(f"Apple GPU matrix multiply: {y.shape}")
print(f"Device: {mx.default_device()}")
Python (PyTorch MPS Backend)
import torch
if torch.backends.mps.is_available():
x = torch.randn(1024, 1024, device="mps")
y = torch.matmul(x, x)
print(f"MPS matrix multiply: {y.shape}")
Architecture Highlights
- Unified Memory Architecture (UMA): CPU and GPU share the same memory pool, eliminating data copies. Especially beneficial for LLM inference — no VRAM bottleneck when loading models
- Metal Framework: Apple low-level graphics/compute API, performance approaching CUDA
- MLX Framework: Apple official ML framework, optimized for Apple Silicon, supporting lazy loading and unified memory
Model Compatibility
| Model/Framework | Support | Notes |
|---|---|---|
| PyTorch | ✅ MPS Backend | Some operators may fall back to CPU |
| MLX | ✅ Native | Apple official framework, recommended |
| llama.cpp | ✅ Metal Backend | Top choice for local LLM inference |
| Llama / Qwen and similar LLMs | ✅ | MLX-LM / llama.cpp both work |
| Stable Diffusion | ✅ | Via MPS / Core ML |
| Whisper | ✅ | MLX-Whisper |
Related Products
If you're evaluating alternatives, the following products may also fit your scenario:
- NVIDIA GPU / CUDA — NVIDIA (GPU Graphics Processor)
- AMD ROCm / GPU — AMD (GPU Graphics Processor)
- Intel Data Center GPU — Intel (GPU Graphics Processor)
- Qualcomm Adreno GPU — Qualcomm (GPU Graphics Processor)
- Moore Threads MTT S5000 — Moore Threads (GPU Graphics Processor)
- Huawei Ascend — Huawei (NPU Neural Processor)