Private Inference Deployment
Model selection and deployment, quantization, compression, monitoring and capacity planning on vLLM and Ollama — exposing an OpenAI-compatible inference service to the agents above it.
Core Advantages
Every Major Open-Weight Model
Private deployment for DeepSeek, Qwen, GLM, Kimi, Llama, Mistral and more — selected against your quality targets and VRAM budget
Throughput and Cost Tuning
AWQ / GPTQ / FP8 quantization, continuous batching and prefix caching to raise concurrency and cut per-token cost on the same hardware
Data Stays Inside Your Boundary
Model weights, inference logs and business data all remain in your environment, meeting finance and healthcare audit requirements
Domain Fine-Tuning
LoRA or full fine-tuning plus preference alignment on your data to lift accuracy on specific tasks
Scenarios
- 1
Data-Sensitive Industries
Finance, healthcare and public sector scenarios with hard constraints on data leaving the premises
- 2
Fully Air-Gapped Networks
Deployment in isolated environments with no external connectivity, including domestic accelerator support
Technical Features
- One-Click Deployment Toolchain
- Hybrid Serving and Autoscaling
Customer Success Stories
State-Owned Bank Intelligent Customer Service
Local model deployment handling 100,000+ daily customer inquiries with 92% accuracy
See how we can build this for youReady to Ship Your Agent
From scenario assessment to production rollout, our delivery team works alongside you. Talk to us for 30 minutes before deciding anything.
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