aicanrun

Local AI compatibility

Can I run AI locally?

Usually yes—the real question is which model and quant fit your device's usable memory, which current app supports that exact file, and whether the resulting speed is worth using. Start with the hardware you own; the report takes you through the download, first prompt, and offline check.

Mac

Check my Mac

Mac mini, MacBook Air, MacBook Pro, Mac Studio, and MacBook Neo by exact unified-memory configuration.

Phone

Check my phone

Detect or choose an iPhone or Android phone, then compare local models and recommended quants.

What decides whether local AI will run?

Step 1

Memory fit

Model weights, KV cache, and runtime must fit after the operating system keeps its share.

Step 2

Quantization

A Q4 model is much smaller than Q8 and is usually the practical local starting point.

Step 3

Memory bandwidth

LLM decode repeatedly reads model weights, so bandwidth strongly affects tokens per second.

Step 4

App support

A file can fit in memory but still need a newer or special runtime. We verify the beginner path separately.

FAQ

Can I run AI locally without an internet connection?

Yes. After you download a compatible model and inference app, generation can happen on your device. Some apps still use the internet for downloads, updates, or optional cloud features.

What matters most for running a local LLM?

Usable memory decides whether the model loads. Memory bandwidth, model architecture, quantization, software backend, and cooling then determine how fast it feels.

How much RAM do I need for local AI?

Small 1–4B Q4 models can work inside roughly 3–5GB. Typical 7–9B Q4 models need around 6–8GB after runtime overhead. Larger 27B and 70B models need much more. The operating system also needs memory, so total RAM is not the same as usable model memory.

Which app should I use?

It depends on the exact file and what you want to do. AICanRun normally uses PocketPal for supported phone GGUF files, LM Studio when a Mac report names an exact GGUF repository and quant, and Locally AI for a model in its curated Apple catalog. Jan is an open-source GGUF alternative, Ollama suits catalog packages and coding/API integrations, and Msty suits mixed engines or document workflows. Special formats are labeled separately.

Are AICanRun speed numbers benchmarks?

Only results explicitly marked verified are measurements. All others are formula estimates or ranges based on model size and memory bandwidth.