🌍 Evrone Turned Private AI into Reality
Many organizations want AI tools, but many organizations also need privacy. That is why Evrone built a private AI assistant running fully inside a client’s own environment.
No external API handled prompts. No third-party cloud stored documents. The company kept everything inside its own network.
What Evrone Built
The assistant could:
- Answer user questions
- Support internal automations
- Run agent workflows
- Connect with approved systems
The Real Challenge
Running one model locally is easy. Running a stable business platform is different. Evrone handled:
- Hardware planning
- GPU workload balancing
- Kubernetes scaling
- Deployment pipelines
- Runtime optimization
Model Testing
The Evrone team compared several open-source tools:
- vLLM
- Ollama
- llama.cpp
- SGLang
They also tested multiple models. Qwen became the strongest production option thanks to reliable quality and compatibility.
Why Speed Matters
A slow assistant frustrates users. Some setups produced only 20 tokens per second. Agent chains became sluggish. Evrone tuned the final environment to about 160 tokens per second, making interactions feel natural.
Current Status
Today the platform works in production with GitOps processes, automated updates, and isolated or hybrid modes depending on business needs.
Final Insight
🔒 Why Companies Choose On-Prem LLM Systems. Private AI is becoming core infrastructure. Evrone showed that secure LLM systems can power real workflows while protecting sensitive information. 🚀🔐