Frontier
vs local.
Once you understand models, training, inference, and chips, one practical question is left: do you rent the world's best model from a frontier lab, or run a capable open model on hardware you control? This is the decision that shapes cost, privacy, and independence — and there's no single right answer.
Every model in the world sits somewhere on a spectrum between two poles. On one end, the frontier (or "pioneer") models — the biggest, smartest systems, reachable only through an API. On the other, local (or "open") models you download and run on your own chips. Same underlying technology; completely different economics and control.
Renting the best brain on Earth
Frontier models — GPT, Claude, Gemini and their peers — are trained by a handful of labs at a scale almost no one else can match: enormous datasets, tens of thousands of top-tier GPUs, budgets in the hundreds of millions. You don't download them. You reach them over an API and pay per token.
What you get is the absolute cutting edge — the most capable reasoning, the broadest knowledge, the newest features, with none of the infrastructure to manage. What you give up is control: your data leaves your building on every request, the price is set by someone else and can change, the model can be updated or retired out from under you, and if their service is down, so are you.
You need maximum capability, you're moving fast, your volume is modest, and your data isn't so sensitive that sending it to a third party is a dealbreaker.
Owning a very good brain outright
Local models — Llama, Qwen, DeepSeek, Mistral and others — are open-weight: the weights file is published, so you can download it and run it on your own hardware. A year or two behind the frontier on the hardest tasks, maybe — but for a huge share of real work (drafting, summarizing, classifying, answering from your own documents, powering an assistant) a good open model is more than enough.
The payoff is ownership. Your data never leaves your network. There's no per-token meter running — once the hardware is paid for, inference is effectively free to run as much as you want. No vendor can change the price, throttle you, or deprecate your model. The cost is that you run the infrastructure: buy or rent the chips, keep it patched, keep it up.
Privacy or data residency matters, volume is high enough that per-token costs sting, you want predictable spend, or you simply want to own your AI stack instead of renting it.
The honest comparison
API models
Most capable · zero infra · pay per token · data leaves your network · price & availability controlled by the vendor.
Open models
Very capable · you run the hardware · fixed cost, no per-query meter · data stays private · full control, full responsibility.
In practice the smartest setups are hybrid: route everyday, high-volume, or sensitive work to a local model, and reach for a frontier API only on the hardest problems where its extra capability earns its cost. You get privacy and predictable spend on the bulk of the work, and the best brain on the planet exactly when it's worth it.
What "local" looks like in the real world
This isn't theoretical for us. Our own AI runs on a rack of local hardware — a small cluster of Mac Studios with 512GB of unified memory each, several NVIDIA DGX Spark units for GPU generation, and a fleet of smaller nodes for embeddings and retrieval. It runs large open models (Qwen, DeepSeek, Llama-class) entirely on-premise, with no API keys and no per-token bill.
The result is exactly the tradeoff described above, lived out: private by default, flat monthly cost instead of a metered one, and complete control over which models run and when. When a task genuinely needs the frontier, we call out to an API — but the day-to-day runs at home. That's the setup we build for clients who want the same independence.
Pick the right path — or let us build it
Choosing between frontier and local (or designing the hybrid) is exactly the kind of decision we help Las Vegas businesses get right — and then implement end to end, from picking models to standing up the hardware.
- Custom AI systems — private models running on hardware you own.
- AI automation — put these models to work inside your business.
- LLM hardware tool — see what it takes to run a given model locally.
- AI solutions overview — the full picture of what we do.
Not sure which side of this line your business belongs on? That's a five-minute conversation. Let's talk →
Common questions
What is a frontier AI model?
A frontier (or "pioneer") model is one of the largest, most capable AI systems — GPT, Claude, Gemini and their peers — trained by a handful of labs at enormous scale. You reach them through an API and pay per token. You get the cutting edge with no infrastructure to manage, but your data leaves your network and the vendor controls price and availability.
What is a local (open) AI model?
A local or open model — like Llama, Qwen, DeepSeek, or Mistral — is one whose weights are published, so you can download it and run it on your own hardware. It may trail the frontier on the hardest tasks, but for most everyday work it's more than capable, and your data never leaves your network.
Is it cheaper to run AI locally?
It depends on volume. Frontier APIs charge per token on every request, which adds up fast at scale. Running an open model locally has an upfront hardware cost but no per-query meter — once the hardware is paid for, inference is effectively free. High volume favors local; low volume often favors the API.
Should I use a frontier model or a local model?
Use a frontier model when you need maximum capability, are moving fast, and your volume and data sensitivity are manageable. Use a local model when privacy matters, volume is high, or you want predictable cost and full control. Many of the best setups are hybrid — local for everyday work, frontier APIs only for the hardest problems.