About Local Frontier

Local Frontier is a practical publication about running open AI models on your own hardware.

New models arrive fast. Model cards list impressive benchmarks. Quantization repos offer dozens of files. Runtime documentation changes. And somewhere between all of that, you still have one simple question:

What should I actually run on my machine?

Local Frontier exists to answer that question.

What we cover

We focus on the practical layer between an open model release and a working local setup: model selection, GPU and memory requirements, quantization, Ollama and other inference runtimes, multimodal input, coding agents, local infrastructure, and the point where renting cloud compute makes more sense than forcing a model onto your own hardware.

The goal is not to cover every AI announcement. We prioritize questions that change a real decision: whether to switch models, which file fits a given GPU, which setting matters, what a feature actually costs in memory or context, and where a vendor claim stops matching the runtime you are using.

How we work

Primary sources come first. That usually means official model cards, repositories, configuration files, runtime documentation, and published file metadata.

When numbers are calculated from those sources, we treat them as derived values rather than vendor claims. When something comes from a real Local Frontier session, we identify it as measured. When a conclusion goes beyond what the source directly states, we call it an inference.

That distinction matters. A download size is not automatically VRAM usage. A capability in a model card is not automatically exposed by every runtime. A vendor benchmark is not the same thing as a result reproduced on local hardware.

We try to keep those boundaries visible instead of smoothing them over.

Local-first, not local-only

Running AI locally gives you control over hardware, privacy, cost, latency, and experimentation. But local is not automatically the right answer to every problem.

Some models are simply too large for consumer hardware. Some workloads benefit from cloud GPUs. Some agent workflows need more context or compute than a laptop can comfortably provide.

Local Frontier covers those trade-offs too. The objective is not to prove that everything should run locally. It is to help you understand where local AI makes sense — and where it does not.

What you can expect

• Hardware-fit and quantization guides based on real file sizes
• Local model setup and runtime guides
• Field reports using retained measurements when available
• Open-model comparisons and upgrade decisions
• Agent, coding, multimodal, and personal AI infrastructure workflows
• Clear separation between vendor claims, measurements, derived values, and inference

Disclosure

Some Local Frontier articles may contain referral or affiliate links. When they do, the relationship is disclosed in the article. Using one of those links may support the site without changing the price you pay.

Commercial links do not replace the technical reasoning behind a recommendation.

Stay on the frontier

If you are building your own AI stack — or just trying to figure out which model will actually fit on the hardware in front of you — you are in the right place.

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