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Articles, guides, and insights on local artificial intelligence
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The in-order path for newcomers to local AI: from basic concepts to installation and your first model.
Optimize performance
Pick the right hardware and get the most out of what you already own: CPU vs GPU, Mac, Raspberry Pi, and fast serving.
Data and privacy
What actually goes over the network with local AI: the data, the security, the compliance, and the control that stays in your hands.
Choosing hardware and model
How to pick the right hardware and the model suited to your use: VRAM, quantization, GPU or CPU and response speed.
7 articles
On-device phone agents: what open mobile agents actually bring (after OpenMobile-2)
OpenMobile-2 opens up mobile agent training environments and recipes, and LFM3-1.7B shows a 1.7GB agent already runs on your phone: numbers, minimum requirements and honest limits.
Local text-to-SQL: query your business database without sending data to the cloud
A local model, the schema to the model, execution on your database: text-to-SQL in-house, ARCS benchmark numbers, and where it really breaks.
MOSS-Image: OpenMOSS-Team's ComfyUI ripack, workflows and real requirements
MOSS-Image: OpenMOSS-Team's ComfyUI ripack, ready in two folders. Real-world VRAM computed per weights combination, from ~11GB to ~31GB, plus the four included workflows.
Local Images: ComfyUI 0.40 and comfy_attention — new for all models
ComfyUI 0.40 extends comfy_attention to all models with per-model selectable kernels and LoRA override, turns on the asset system by default with a local API, and standardizes 3D output; the VRAM table stays the choice reference.
ML agents that run locally: MLExpert and EpochGo on Ollama, zero API costs
MLExpert raises the bar to 35B and a 72.7% medal rate; EpochGo runs three AutoML agents on 16 GB at zero API cost: what works today, the math and the limits.
Decision models in local: JEV-35B and the 3B-active System 1
You don't need a big LLM to decide. JEV-35B (35B, 3B active) in NVFP4 on a 32GB GPU and ej (11.4MB) on CPU show how to do routing, classification and quality control without generating a single token.
Costs: local vs API, done with real numbers
How much does local really cost compared to a cloud API? Verified per-token prices, GPU amortization, electricity and the break-even point, calculated with real numbers.