Engineering insights, tutorials, and product updates.
Introducing Macyou v2 — browse 50+ pre-configured AI models in our catalog, access every deployment via an OpenAI-compatible API, and pick from five tiers starting at $149/mo.
The M4 Pro chip delivers 38 TOPS of neural engine performance. Here's why dedicated Apple Silicon is replacing cloud GPUs for AI agents and local LLM inference.
Step-by-step guide to running Llama 3.2 with Ollama on Apple Silicon in the cloud. From zero to 47 tokens/second.
Run Meta's Llama 3.1 8B on dedicated Apple Silicon. 47 tok/s on M4 Pro, 16 GB RAM, perfect for AI agents and chatbots — no GPU rental needed.
Deploy Mistral 7B on a dedicated Mac Mini M4 Pro. Compact, fast, and great at following instructions — ideal for structured AI tasks.
Deploy DeepSeek R1 8B on M4 Pro for strong reasoning performance. 8B params, 16 GB RAM, built for multi-step logic and math tasks.
Run Alibaba's Qwen 2.5 32B on a 32 GB Mac Mini. Strong multilingual support, coding ability, and 32B-parameter depth for complex tasks.
Deploy Mistral Small 24B on a 32 GB Mac Mini. A compact 24B model with a permissive license, ideal for production workloads and commercial use.
Run the full Llama 3.3 70B (Q4 quantized) on a 64 GB Mac Mini. The best open-weight model at this scale, now on dedicated Apple Silicon.
Deploy Mistral Large 2 (70B) on a 64 GB Mac Mini. Mistral's most capable model for reasoning, code, and multilingual tasks — no GPU cloud needed.
Run Qwen 2.5 72B at full precision on a 96 GB Mac Mini. Best-in-class multilingual performance with no quantization compromises.
There is no 70B version of DeepSeek V3. What you want is the R1 Distill 70B: DeepSeek-level reasoning at Q4 on a 64 GB Mac mini M4 Pro. Here’s the honest setup.
Llama 3.1 405B at Q4 needs ~245 GB of memory — no 128 GB Mac runs it. What does: an M3 Ultra 256 GB at the edge, or a 2-node Thunderbolt 5 cluster. Real numbers inside.
DeepSeek V3 is 671B parameters (37B active). A Q4 build is ~400 GB — it takes a Thunderbolt 5 cluster of M3 Ultras, not a single Mac. The honest math and the alternatives.
How to set up a fast, reliable iOS build pipeline using GitHub Actions self-hosted runners on Macyou's M4 Pro servers.
Virtual Mac instances share resources and throttle performance. Here's our technical argument for bare-metal Apple Silicon.
Apple’s MLX framework is purpose-built for Apple Silicon. Learn how to deploy MLX on a dedicated M4 Pro server for fast ML training and inference with Metal acceleration.
Deploy Ollama on a bare-metal Mac Mini M4 Pro and serve Llama, Mistral, and Gemma models via API. No shared GPUs, no CUDA, no overhead.
Set up ComfyUI with Metal GPU acceleration on a dedicated Mac server. Build Stable Diffusion workflows with nodes, no NVIDIA required.
What LM Studio needs on Apple Silicon (16 GB minimum for 7B models), measured tokens/sec on M4, and how to run it on an always-on cloud Mac with an OpenAI-compatible endpoint.
Deploy OpenAI’s Whisper model on a dedicated M4 Pro server with MLX acceleration. Batch-transcribe audio 4x faster than CPU-only cloud instances.
Generate images with Stable Diffusion on a dedicated Mac Mini M4 Pro. SDXL, ControlNet, and LoRA support — all accelerated by Metal.
Run llama.cpp with Metal GPU acceleration on a cloud Mac Mini. GGUF models, minimal overhead, and raw inference speed on dedicated hardware.
Spin up a fresh macOS Sequoia server on dedicated Apple Silicon hardware. Full SSH and VNC access, configure it however you want.