The Locally Hosted AI — Page 3

Calculate model size and context demands before buying hardware to avoid costly mistakes.

Unified memory, not GPU cores, determines whether local LLMs even run on Apple Silicon.

Data privacy and cost savings drive the shift from cloud AI to self-hosted hardware.

Qwen2.5 outperforms Mistral Small for most multilingual local deployments.

Different formats trade speed, memory, and quality depending on your hardware.

KV cache size varies wildly by architecture, making file size alone a poor guide to what fits.

Instruction-tuned models work out of the box; base models need real setup.

Local processing keeps sensitive documents off third-party servers.

Llama 3 excels at reasoning and long context, while Mistral trades depth for speed and efficiency.

Four architectural approaches to building durable memory for local open-weight models.

Privacy and control matter more than convenience when your documents never leave your own server.