A practical guide to local AI.
What running AI models on your own hardware actually means, what it takes, and where its limits are — without the hype.
What local AI is
Running an AI model directly on hardware you own and control, rather than sending requests to a cloud API. The model, its weights, and its execution all live on your machine.
The benefits
Data stays on your hardware unless you choose to send it elsewhere. No per-request cloud cost or rate limit. Works without depending on an internet connection or a third party's uptime. Full control over which model version you run and when it changes.
The limits
Local hardware has a ceiling — the largest, most capable frontier models generally still require cloud-scale infrastructure no consumer or workstation GPU can match today. Local AI is genuinely powerful for a wide and growing range of tasks, not a full replacement for every cloud capability.
GPU and VRAM basics
A model's active memory footprint has to fit in GPU VRAM to run efficiently. More VRAM generally means larger models or longer context windows are practical locally. This is why GPU memory capacity is the headline spec for local AI capability across the ER catalog, not raw clock speed.
RAM and storage considerations
System memory and fast NVMe storage matter too — for loading models quickly, for running supporting software alongside the model, and for datasets that don't fit entirely in GPU memory.
Local vs. cloud
Not a binary choice. Local is often better for latency-sensitive, privacy-sensitive, or offline-required work; cloud is often better when you need capability beyond what local hardware can deliver, or need to scale beyond one machine. Most real workflows use both.
Why ER tiers exist
Local AI capability tracks GPU memory and platform class. Every ER product page shows a descriptive Local AI capability rating (Capable / Advanced / Extreme / Flagship) based on the actual hardware in that system — never a specific unverified model-size promise.
The ASH A(π) distinction
ASH A(π) is the one system in the catalog built around native ASH intelligence from the ground up, with 96GB of ECC GPU memory specified for that role. Every other ER system is genuinely local-AI-capable; ASH A(π) is the only one that is ASH-native.