--- title: "On-Premise LLM Deployment: vLLM + Triton + your VPC" description: "Self-hosted inference on Llama, Mistral, Qwen. vLLM + Triton + Kubernetes. GPU sizing, autoscaling, air-gapped where required. Eight weeks from weights to production." source: "https://www.kensink.com/llm/on-premise/" canonical: "https://www.kensink.com/llm/on-premise/" --- ★ On-premise LLM Direct LLM · no framework 8-week engagement ON-PREMISE · PRIVATE LLM DEPLOYMENT # Run your own weights. Your VPC. Your call. Self-hosted inference on Llama, Mistral, Qwen, or your fine-tuned variant. vLLM + Triton for production throughput. GPU sizing that doesn't melt your finance team. Air-gapped deployments where the contract requires it. Python vLLM Triton Kubernetes [Start this engagement →](https://www.kensink.com/contact) [All LLM services →](https://www.kensink.com/llm) Cycle 8 weeks · weights to live Stack vLLM · Triton · Kubernetes Output Inference cluster + autoscaler + dashboards Compliance Air-gap-capable, data never leaves \[WHY THIS EXISTS\] ## Some data cannot leave the building. Healthcare records. Defense workloads. Regulated finance. The hosted-API answer doesn't exist for these problems. You need the weights in your VPC, the GPUs under your control, and a deployment your security team can audit end-to-end. - Frontier-grade open weights (Llama 3, Qwen 2.5, Mistral Large) on your hardware - Latency-aware request batching for production throughput - GPU autoscaling tied to actual demand, not vendor minimums - Air-gapped deployment patterns where the contract requires it \[HOW WE BUILD IT\] ## Boring infra. Frontier models. 01 ### vLLM as the engine Paged attention, continuous batching, tensor parallelism. The serving stack that powers most production open-weight deployments today. 02 ### Triton for orchestration NVIDIA Triton Inference Server in front of vLLM. Model routing, ensembles, dynamic batching, metrics. Kubernetes-native. 03 ### Right-sized cluster We benchmark your actual traffic before quoting GPU hours, then pick the A100, H100, or L40S that matches the latency, throughput, and budget targets. 04 ### Observability + cost Prometheus + Grafana for inference metrics. Per-tenant cost rollups. Token-per-second SLOs. The same dashboards your ops team already runs. \[ OUTCOMES AT HANDOFF \] ## What's live at week eight. 0 bytes Of prompt data leaving your VPC ~200 tok/s Sustained throughput on H100s <1.5s P95 latency on 7B-class models 100% Source ownership of the deployment \[ALSO WORTH READING\] ## Related LLM engagements. [ ENTERPRISE LLM Enterprise LLM Read the engagement ](https://www.kensink.com/llm/enterprise)[ FEEDBACK TRAINING Fine-tuning Read the engagement ](https://www.kensink.com/llm/fine-tuning)[ OBSERVABILITY LLM Observability Read the engagement ](https://www.kensink.com/llm/observability) DIRECT LLM · APPLIED K ## Bring the problem. We’ll bring the build. Eight weeks, fixed price, eval suite at handoff. Direct LLM engineering on top of the K-Framework. Two Q3 slots remain. [Start this engagement →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)