Apple silicon for AI.
Always on, always yours.

Unified memory changes the economics of local inference.

Running LLMs locally usually means renting GPU instances at £1–£5/hour. Apple silicon takes a different approach: unified memory means the CPU and GPU share the same RAM, so a 24GB M4 Pro can load models that would need an expensive GPU on x86 hardware.

What you can run on Apple silicon:

• 7B–13B parameter models comfortably on 16GB (Budget/Starter tiers)
• 30B+ parameter models on 24GB unified memory (Pro/Max tiers)
• Ollama, llama.cpp, MLX — all optimised for Apple silicon
• Stable Diffusion via Core ML — fast image generation without a discrete GPU
• Whisper — real-time speech-to-text on-device

The cost comparison is stark. A dedicated Mac mini running inference 24/7 costs £39–£129/mo. An equivalent GPU instance on AWS or Lambda Labs runs £700–£2,000/mo.

What our customers run.

• AI agents and automation — browser-based agents (OpenClaw, Playwright) combined with local LLM reasoning
• Private inference — models running on your own hardware, no data leaving the machine
• Fine-tuning experiments — MLX makes fine-tuning practical on Apple silicon
• RAG pipelines — embedding models + vector search + local inference, all on one box
• Development and prototyping — iterate on prompts and model configs without burning API credits

Every machine is dedicated to you — no noisy neighbours, no shared GPU, no throttling. Your models load once and stay resident.