Articles from the team on deploying, sizing and operating open-weight LLMs on your own GPUs.
Latest of 7Private AI, Explained: Self-Hosted LLMs and When They Beat an API24 Oct 2026
Private AI means the model runs inside your perimeter, on hardware you control — not a vendor's "private" tier. What it actually involves, the real reasons to self-host open-weight LLMs, and the honest cases where a cloud API still wins.
An on-prem open-weight model cascade applied to a real B2B contractor portal — seven jobs handed to the models, the five roles it lifts load from, and a transparent model of the payroll impact.
Mixture-of-Experts models like Llama 4, Qwen3 and DeepSeek report huge parameter counts but activate only a fraction per token. Here's how to size GPUs for them correctly.
A practical, no-hype comparison of the three leading open-weight model families for enterprise on-premises deployment — architecture, hardware, and the workloads each one wins.
When does buying GPUs beat paying per token? A grounded total-cost-of-ownership model for self-hosting open-weight LLMs versus commercial cloud APIs in 2026.
How we built ephemeral, self-hosted open-weight inference for Infiano's hackathon — 40 rented GPUs, a custom 20B model, and why it beat a cloud API on both cost and control.
Data residency, the EU AI Act, GDPR and sector rules are pushing regulated enterprises toward on-premises AI. What the regulations actually require, and how self-hosting maps to them.
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