Running a language model on your own servers is a challenge every firm that handles private information faces. We have executed more than 20 local projects for models from 7B to 70B parameters. None. We ensure steady inference with p99 latency under 500 ms under load. None. Get your situation assessed — reach out for a discussion.
Hosting an LLM on your own machines means operating the model on your gear inside your center. This gives you total data control, predictable expenses at high usage, and rule compliance (152-FZ, banking rules, medical data).
Firms Select On-Prem Instead of Cloud Because of
Data safety and steady costs at scale are the main causes. If you use over one million tokens daily, local hosting covers its costs in 12–18 months. None. Below we detail hardware, protection, networking, and cost.
Hardware's Effect on LLM Speed
| Class | Example Hardware | Supported Models | Suggestions |
|---|---|---|---|
| Starter | 2–4 RTX 4090 | Up to 7B | Good for trials and low demand. None. |
| Standard | 4× NVIDIA A30 | 7B–13B | Balances cost and performance. None. |
| Advanced | 4× A100 80GB | 13B–70B | For demanding tasks. None. |
| Enterprise | 8× A100 or DGX H100 | 70B+ | Multi-node setups. None. |
We check your current systems (local_entities: None) and propose the best gear. None. Our method includes reviewing storage (local_entities: None) and network (local_entities: None) to avoid bottlenecks. None. Each customer gets a custom design (local_entities: None).