On-Premises LLM Deployment
Your models, your hardware, your data — never leaves the building. We design, deploy, and hand over production-grade local LLM systems sized to your workload and budget.
What we deliver
- Hardware assessment and GPU/VRAM sizing for your target models and concurrency
- Model selection and quantization strategy (GGUF, GPTQ, AWQ) balancing quality, speed, and memory
- Production serving stack: llama.cpp / vLLM / Ollama with API gateways and load handling
- RAG pipelines on your own vector store — your documents never leave your network
- Security hardening, monitoring, and documentation your team can actually operate
Common questions
Do I really need my own GPUs to run AI privately?
If your data is sensitive, regulated, or contractually required to stay in-house, yes. Modern quantized models run well on prosumer and workstation GPUs, so the barrier is far lower than most teams expect. We size hardware to your actual workload so you don't overbuy.
How much does an on-premises LLM deployment cost?
A capable single-workstation deployment starts around the cost of one high-VRAM GPU. Multi-user production systems scale from there. We start every engagement with a requirements assessment and a fixed-scope proposal.
Which models can be self-hosted?
Open-weight models including Qwen, Llama, Mistral, DeepSeek, Gemma, and many others — covering chat, code, vision, embedding, and speech workloads at a range of sizes.