[miktam — preface]

Companion to Every Company Can Be a Palantir Now. That essay made the institution-level case; this one extends it to the individual. Same physics, smaller perimeter. The argument is strategic; the hardware that tests it sits on my desk.

— miktam

This essay argues that local, sovereign intelligence can now rival centralised, cloud-based models in specific contexts, shifting some control from institutions back to individuals.

The Physics of the Local Engine

Let’s start with hardware. NVIDIA spent decades solving a hard physics problem: maximising compute and memory bandwidth between CPU, GPU, and system memory. A single Nvidia B200 GPU delivers around 8 terabytes per second of memory bandwidth — an order of magnitude beyond typical consumer hardware.

Apple’s approach is different. By unifying CPU, GPU, and Neural Engine into a single system-on-chip with shared memory, Apple Silicon enables highly efficient local inference. While it does not match datacenter GPUs in raw performance, it makes powerful, zero-cloud compute accessible on consumer hardware.

Orchestrating the Architecture

Once hardware is sufficient, the challenge shifts to orchestration. Open-weight models like Gemma, Llama, and Qwen are already highly capable, but running them locally requires careful design to avoid inefficiency.

A common approach is cascading: routing most requests through a smaller, faster model and escalating only when needed to a larger one. With well-designed constraints and feedback loops, this keeps latency low while preserving capability.

The Leviathan vs Data Sovereignty

The real shift is not hardware — it is data.

Cloud-based models require sending data to centralised providers. Local models invert this: all data remains under your control.

The key question becomes: can a model with full access to your private context outperform a more powerful model that lacks it?

In many cases, it already does.

The Real Constraint

Not all knowledge is digital. Much of it is tacit, fragmented, or poorly structured. Local models do not solve this automatically.

But where data is rich, consistent, and private, it has a structural advantage.

A frontier model without access to your financial history, health data, or internal metrics operates with partial visibility. A smaller local model, tightly coupled with that data, can reason more effectively within that domain.

The Shift

General intelligence is becoming commoditised.

As it does, value shifts toward:

  • proprietary data
  • high-quality context
  • and control over both

This is not entirely new. But it is becoming operationally real.

The trade-off between privacy and capability is no longer absolute.

Every company can be a Palantir.

And increasingly, every individual can build one.