Local In-House Model Hosting
Relying exclusively on external API providers can introduce security leaks and unpredictable query expenses. We design, benchmark, and configure private cloud hosting templates for open-weight models (like Llama and Mistral), validating performance parameters before live operations.
We configure optimized local inference containers (e.g. using vLLM or Ollama runtimes) to host top-performing open-weight models, including Llama 3.1 70B/405B, DeepSeek-R1, Mistral Large 2, and Qwen-2.5-Coder-32B. This setup ensures that commercially sensitive data remains fully isolated in your private infrastructure.
Sovereign Infrastructure Hardware & Model Guide
Our benchmarks guide server procurement, GPU selection, and optimized runtime containers (specifically deploying NVIDIA NIM microservices from build.nvidia.com for local execution):
| Model Sizing | Recommended GPU Config | Minimum VRAM | Primary Use Case Placement | Inference Runtime / NIM Container |
|---|---|---|---|---|
| Llama 3.2 3B / 8B | 1x NVIDIA RTX 4090 / A10G | 16GB - 24GB | High-speed query sanitation, input filtering, PII screening | Llama-3.2-8b-instruct NIM |
| Qwen-2.5-Coder 32B | 1x NVIDIA H100 / 2x A10G | 40GB - 80GB | Automated SQL code generation, catalog schema writes | Qwen-2.5-Coder-32b NIM |
| Llama 3.1 70B / DeepSeek-R1 | 2x NVIDIA H100 / A100 | 160GB VRAM | Technical report synthesis, engineering SOP RAG search | Llama-3.1-70b-instruct NIM / DeepSeek-R1 NIM |
| Llama 3.1 405B / Mistral Large 2 | 8x NVIDIA A100 / H100 | 640GB VRAM | Strategic decision support, complex multi-dataset reasoning | Llama-3.1-405b-instruct NIM |
Sovereign Cloud Deployment Framework
Our deployment framework guides the setup of private inference endpoints securely:
1. Environment Isolation
We deploy models inside secure Docker containers on private networks, blocking all external telemetry and model training collection checks.
2. NVIDIA NIM Integration
We deploy pre-packaged NIM containers from build.nvidia.com, utilizing TensorRT-LLM and vLLM engines optimized for maximum token throughput on local GPUs.
3. Failover Configurations
We wire the AI gateway to automatically fall back to secure secondary servers if local GPU clusters hit queue bottlenecks, preventing downtime.