Most AI providers offer privacy guarantees, however these are always contractual and customers are dependent on the vendor to honour and deliver technically on the guarantees. Disconnected AI flips this model whereby privacy is enforced automatically by the architectural decisions, not merely by a contract between parties. When running models locally on Apple Silicon: 

• No prompts are logged externally 

• No training data is reused without your consent (whilst many paid Enterprise AI models make this commitment, shadow IT usage of third-party AI agents don’t make this promise) 

• No third-party visibility exists in your workflows or has access to your data 

Disconnected AI moves privacy from a legal promise to an architectural certainty. 

This is a fundamental shift in thinking that security-conscious organisations need to consider. Instead of trusting providers not to misuse data or rely on their security defences to not be breached, exploring how Disconnected AI could enable your organisational AI workloads has the potential to eliminate this possibility altogether. 

Apple has earned many plaudits from industry analysts for their decision to build their own silicon based on ARM architecture processors and, with the rise of AI workloads, their M series chipsets have quietly become one of the most efficient AI inference platforms available. This is powered by: 

• Unified Memory architecture 

• High performance GPU and Neural Engine 

• Exceptional power efficiency, even under load 

Unlike discrete GPU systems, Apple Silicon’s unified memory allows the CPU, GPU and Neural Engine to access the same memory pool without duplication, removing a major bottleneck in model inference where weights must otherwise be copied between memory domains. 

This unlocks modern Macs to run sophisticated LLMs with performance levels that are often surprising to end users. RAM requirements on Apple’s Unified Memory architecture should be considered carefully: 32GB could run 7-13B (billion) parameter models, whereas 128GB+ is required for larger models. LLM’s can range in size from lightweight (7B parameters), through to the practical sweet spot for Disconnected AI (around 13B) to the massive frontier models (70B). A 13B parameter model running on an M3 Ultra can achieve ~30 tokens/second making interactive workloads viable for knowledge workers accessing the LLM.  

Beyond the Apple Silicon performance, further gains are achieved because latency drops dramatically as there is no network roundtripping of queries, offline capability becomes viable (workloads can continue with or without internet connectivity) and developers can experiment freely without cost constraints hanging over them. 

As demand increases, modular scalability can be achieved very simply with macOS, whereby multiple Macs can be clustered together simply through the use of Thunderbolt cables. This effectively pools the memory and compute resources from multiple Macs, with open-source software distributing the AI workloads across the clustered hardware allowing for easy and cost-effective scaling incrementally with predictable costs based on demand. 

A distinct shift has occurred with commercial AI tooling in the last six months, towards allowing end-user choice for underlying models, with examples of this prevalent in coding and generative media apps specifically. Often, however, organisations are paying only for a single generative AI application for knowledge workers, creating a degree of vendor lock-in and organisational exposure to pricing changes. By contrast, Disconnected AI unlocks the freedom to choose from various models as needed, with the ability to fine-tune performance for proprietary use-cases.  

The flexibility to switch models without vendor friction is a feature of Disconnected AI, allowing experimentation to flourish as team members can test multiple models side by side and continue to iterate without incurring additional costs. This flexibility reaffirms the continued need for hybrid workloads as ultimately this is not a cloud vs local AI argument, but instead a call for intentional decision making when deploying AI workloads: 

• Cloud AI for hyperscale, collaboration and external facing use cases 

• Local AI for sensitive, high frequency or cost-intensive workloads 

As AI becomes embedded into an increasing number of workflows, the question is no longer what you can build, but where it runs and who controls it. This is evidenced by the increasing number of requests for proposals (RFPs) asking questions around Data Sovereignty as part of the response. Exploring Disconnected AI solutions with Apple Silicon represents a shift towards ownership over access, privacy over policy and predictability over consumption. 

While local models offer strong performance for summarisation, coding assistance and retrieval tasks, they still lag frontier cloud models in reasoning depth and multi-modal capability. Critically, however, in the current economic climate with soaring compute costs for the foreseeable future, Apple have lowered the barrier to entry with their remarkable M series chipsets allowing for practical, scalable and increasingly compelling AI solutions to be run locally.  

In the next phase of AI adoption, the real competitive advantage won’t just be intelligence – it will be who owns it.

Keen to learn more about Disconnected AI solutions running on Apple silicon?

Reach out to Cyclone’s Apple Practice Team.