February 20, 2026 · Software · 2 min read
Why Local AI Matters
The case for running AI models on your own hardware — privacy, speed, and independence.
Every major AI product today works the same way: your data goes to a server, gets processed, and the result comes back. For most use cases, this is fine. For email, it's a problem.
The Privacy Argument
Your email contains everything — financial statements, medical records, personal conversations, business negotiations. Sending all of that to an AI provider's servers for "smart features" is a trade-off most people don't think about.
Charger Mail takes a different approach. All AI features run through Ollama on your local machine. The models are smaller than GPT-4 or Claude, but for email-specific tasks — summarization, reply drafting, priority sorting — they're more than capable.
The Speed Argument
Cloud AI has latency. Even with fast servers, you're looking at 200-500ms round trips before you see the first token. For an email client where you want instant responses to keyboard shortcuts, that lag is noticeable.
Local inference on Apple Silicon is fast. An 8B parameter model on an M-series chip generates tokens at 30-40 per second with sub-100ms time to first token. For short tasks like "summarize this email in one sentence," the response feels instantaneous.
The Independence Argument
Cloud APIs change pricing. They go down. They deprecate models. They change terms of service. Building on local inference means your app works regardless of what any AI company decides to do tomorrow.
The trade-off is capability — local models aren't as smart as the frontier cloud models. But for focused, domain-specific tasks, the gap is smaller than you'd think. And it's closing fast.