Engineering insights, tutorials, and product updates.
Which Mac runs which model: 16 GB handles 7B to 14B (21 to 47 tok/s measured), 64 GB M4 Pro runs 70B, 256 GB M3 Ultra 200B. When a cloud Mac makes sense.
Introducing Macyou v2 — browse 50+ pre-configured AI models in our catalog, access every deployment via an OpenAI-compatible API, and pay a fixed chip-based price — M4 Mac mini from $80/mo billed annually or $99/mo billed monthly.
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.
OpenClaw monthly cost: the machine (VPS, Mac mini or a rented Mac) plus tokens or a local model, worked examples and what NVIDIA Vera Rubin may change.
Any Mac mini runs OpenClaw with a hosted model. For local models memory decides: which M6 or M5 Pro fits Gemma 4 or Qwen3.6 at 64K context, and how fast.
Meta's 816B Muse Spark Open needs about 493 GB at 4 bit. Apple ran it on two M5 Ultra Mac Studios at 25 tok/s. Which Mac fits each version, and what it costs to try.
EC2 Mac prices for all nine instance types, from $0.65 per host hour (M1) to $12.50 (M3 Ultra), per month and per 24 hour minimum, from AWS's own price feed. Plus what storage and egress add, and what the same Mac costs by the month.
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 about 245 GB of memory, so no 128 GB Mac runs it. What does: an M3 Ultra with 256 GB at the edge, or a two node cluster.
DeepSeek V3 is 671B parameters (37B active). A Q4 build is about 400 GB, so it takes a cluster of M3 Ultras, not one Mac. The 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 built for Apple Silicon. How to deploy MLX on a dedicated M4 Pro server for 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.
Step by step LM Studio tutorial for Apple Silicon Macs: install, which models fit in 16, 32 and 64 GB, and measured speeds (Llama 3.1 8B at 21 tok/s on an M4). Plus how to keep it always on behind an OpenAI compatible API.
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.
How llama.cpp uses Metal on Apple Silicon, the flags that matter, what a base M4 and an M4 Pro 64 GB measure on 8B and 70B, and when to pick Ollama.
Spin up a fresh macOS Sequoia server on dedicated Apple Silicon hardware. Full SSH and VNC access, configure it however you want.