Why Local

Why Run AI Locally?

New to Local AI? This page will get you oriented.

What Is Local AI?

Most people experience AI through cloud services — ChatGPT, Claude, Gemini, Copilot.

You send a prompt to a remote server. They process it. They return a response.

Local AI flips that model.

Instead of sending your data to someone else’s infrastructure, you download a model and run it directly on your own machine. Your prompts never leave your computer. There is no API call, no external logging, and no third-party processing.

You own the inference layer.

Why Does It Matter?

1. Privacy by Default

Cloud AI requires trust.

Even with strong policies, your data:

• Traverses third-party infrastructure

• May be logged or retained

• May be subject to training reuse

• Is vulnerable to breach or subpoena

Running locally means your AI conversations are as private as your own files. For sensitive work — legal, financial, healthcare, proprietary code — that matters.

2. Cost Control and Predictability

Cloud AI is priced per token.

The more you use it, the more you pay.

Local AI shifts the model:

• Hardware becomes a capital asset
• Marginal query cost trends toward zero
• No per-call billing
• No surprise invoices

If LLM usage is a growing line item on your P&L, local inference can convert variable OPEX into predictable infrastructure spend.

3. Offline and Resilient

Local models:

• Work without internet
• Run in secure or air-gapped environments
• Continue working if providers throttle, rate-limit, or go down

You are not dependent on someone else’s uptime.

4. Control and Permanence

Cloud providers:

• Deprecate models
• Change pricing
• Modify safety layers
• Restrict use cases

A model downloaded to your machine remains yours.

You choose when to upgrade.

You choose how it behaves.

5. Architectural Freedom

Running locally enables:

• Full pipeline control
• Custom fine-tuning
• Private embeddings and retrieval
• Integration into internal systems without API latency
• Deterministic workflows around LLM outputs

You’re not just consuming AI — you’re engineering with it.

What Can You Actually Do Locally?

Modern local models can:

• Hold conversations
• Summarise large documents
• Extract structured data
• Generate and refactor code
• Analyse sensitive datasets
• Run private copilots over internal knowledge bases

For many everyday and professional workloads, the quality gap between cloud and local models has narrowed significantly.

Not every frontier task can be matched — but most production use cases don’t require frontier reasoning.

What Do You Need?

You don’t need a data centre.

Rough guide:

• 8GB RAM → Small models for lightweight tasks
• 16GB RAM → Solid general-purpose local models
• 32GB+ RAM or GPU → Faster inference and larger models
• Dedicated GPUs → Serious local deployment or multi-user setups

Most modern laptops can run useful models today.

Who Is Local AI For?

Local AI is especially powerful for:

• Developers building private tooling
• Startups managing inference costs
• Enterprises handling regulated data
• Researchers working with sensitive material
• Individuals who care about digital sovereignty

If your AI usage is operational — not just experimental — local options deserve serious evaluation.

Getting Started

There are multiple ways to run local models:

• Lightweight CLI tools
• Desktop apps
• Docker-based deployments
• GPU-backed servers
• Embedded inference in production pipelines

Start simple. Experiment. Compare models. Measure performance.

Local AI is a spectrum — not a single configuration.

The Strategic View

Cloud AI is incredible for:

• Capability discovery
• Rapid experimentation
• Frontier reasoning

Local AI is powerful for:

• Operationalisation
• Cost control
• Privacy-sensitive workflows
• Long-term platform ownership

The smart strategy is not “cloud vs local.”

It’s knowing when each makes sense.

Welcome

This site exists to explore, document, and explain the local AI ecosystem:

• Model releases
• Hardware builds
• Deployment patterns
• Performance benchmarks
• Security considerations
• Production architecture

Whether you’re running your first model on a laptop or designing a multi-node inference stack, you’re in the right place.

Welcome to LFTW