Cashcrown // ai.infra
the minimum sensible hardware.
19/20 in this consoleSovereign AI infrastructure
You need “GPU selection / sizing”, but building it in-house gets stuck on integrations, maintenance and lack of time - and the result tends to be fragile and hard to scale.
the minimum sensible hardware. We deliver it as part of “Sovereign AI infrastructure”: a working system with observability, safety gates and documentation. Models are always reached through the router: we mask PII before it leaves for the cloud.
Choosing models and hardware to fit real load and budget.
LLM serving + embeddings, fronted by a router/gateway.
Document indexing, semantic search, answers with citations.
Observability, backups, network isolation, cost control.
Reference profile · Tier: mid · P50: 540 ms
Recommended tier: mid (median P50 latency: 540 ms, reference cost: $1/1M tokens).
Not necessarily. We pick a variant to match the load: from small models on a CPU/single GPU up to a cluster. What matters is predictable cost, not maximum hardware.
The router lets you mix: local models for sensitive paths, the cloud where you need raw power. No lock-in.
In the on-prem variant: no. We mask PII before anything leaves for the cloud.
It depends on the variant: from small models on a CPU to a cluster. We aim for predictable monthly cost, not maximum hardware; local can be cheaper and safer than an API at steady, high traffic. Compare local vs cloud in the inference calculator, and we start with a fixed-cost pilot.
Yes. Self-hosting and PII masking keep sensitive data local (it can stay in-country), and the router gives you an audit trail. We design transparency and human oversight in from the start; profiling or decisions about people add a DPIA.