🤖 How Evrone Built a Real Private AI Platform
Modern companies want AI power without sending sensitive data to external providers. Evrone worked on exactly that challenge: building a fully private AI assistant that runs inside the client’s own infrastructure.
This was not a simple “run a model on a laptop” case. Evrone designed a complete internal platform with servers, orchestration, deployment pipelines, and agent workflows. The assistant had to answer natural-language questions, automate tasks, and connect with internal tools.
Main Challenges
- Hardware Selection
Evrone evaluated infrastructure able to support stable inference under real load. - Software Environment
Evrone configured Kubernetes, deployment flows, monitoring, and model runtimes. - Model Compatibility
Different formats required testing: Safetensors, GGUF, and others.
Why Software Matters
Hardware gives power, but software gives reliability. Evrone tested popular runtimes such as vLLM, Ollama, llama.cpp, mistral-rs, and SGLang. Each tool had strengths, but no universal standard existed.
Final Working Stack
After benchmarks, Qwen became the preferred model because of its balance between speed and quality. SGLang became the runtime choice because it handled multiple formats well in Linux environments.
Some early setups reached only 20 tokens per second. That looked acceptable in theory but felt slow in real agent workflows. Evrone optimized the final configuration to around 160 tokens per second, creating a much smoother experience.
Current Result
Today the system runs in production. Evrone implemented:
Why It Matters
Private AI is no longer experimental. Evrone showed that companies can deploy serious LLM systems on-prem and keep full control over data, compute resources, and long-term evolution. 🔐🚀