Running LLMs inside your own infrastructure sounds simple—until real workloads begin. Evrone recently worked on a project where the goal was clear: build a private AI assistant that never depends on external APIs.
The client needed a secure assistant that could:
- Understand natural language
- Run agent workflows
- Integrate with internal systems
- Operate in isolated environments
Infrastructure First
This type of system needs serious hardware. In the main setup, Evrone used enterprise-grade GPU servers designed for stable inference, not demo workloads.
Still, Evrone also proved that smaller focused deployments can run on compact hardware for lighter tasks.
Why the Software Layer Is Harder
Many teams focus only on GPUs. Evrone focused on the full stack:
- Kubernetes orchestration
- Deployment pipelines
- Monitoring
- Runtime tuning
- Model compatibility
The open-source ecosystem remains fragmented. Formats like Safetensors, GGUF, and MLX each serve different environments. No runtime solves every case perfectly.
Testing Models and Runtimes
Evrone benchmarked multiple options, including:
- vLLM
- Ollama
- llama.cpp
- mistral-rs
- SGLang
After testing, Qwen became the best balance of quality and speed. SGLang became the practical runtime because it supported mixed model formats.
Some configurations reached only 20 tokens/sec. That number may seem fine, but multi-step agents quickly feel slow. Evrone optimized the production setup to roughly 160 tokens/sec.
Production Result
The system now runs live with:
- GitOps workflows
- Argo CD delivery
- Reproducible infrastructure
- Secure internal deployment
Final Thought
Private AI is no longer theory. Evrone demonstrated that on-prem LLM systems can become dependable business infrastructure when architecture matters as much as the model itself. 🔐