abeebaPrivate & On-Prem LLM Deployment
We deploy open-weight language models such as Llama, Mistral and Qwen on hardware you control: your data center, your private cloud, or a fully air-gapped network. Prompts, documents and outputs never leave your boundary.
What's included.
Model selection & sizing
We benchmark candidate models on your real tasks, then size GPUs, memory and throughput before you commit to hardware.
Production inference serving
vLLM, TGI or Triton with continuous batching, quantization and autoscaling tuned to your traffic shape.
Private retrieval (RAG)
Search over your own documents with a self-hosted vector database, row-level access control and cited answers.
Internal AI gateway
One OpenAI-compatible endpoint with SSO, per-team keys, rate limits, audit logs and usage reporting.
How the engagement runs.
Discover & benchmark
Map use cases, data sensitivity and load. Test 3–5 models against your own evaluation set.
Architect & size
Choose on-prem, colocation or private cloud. Produce a capacity plan and bill of materials.
Deploy & harden
Infrastructure as code, serving stack, gateway, monitoring and security controls go live.
Hand over & operate
Runbooks, training and on-call handover, or we stay on under a managed plan.
What you keep at the end.
- Architecture document and capacity plan
- Terraform and Helm code in your repository
- OpenAI-compatible internal endpoint
- Evaluation report comparing candidate models
- Runbooks, dashboards and alert rules
Tools we commonly use
We pick tools to fit your environment and team. These are the ones we use most often.
Can the system run completely offline?
Yes. We regularly design air-gapped deployments where weights, containers and updates arrive through a controlled transfer process and nothing calls out to the internet.
Which models can you deploy?
Any open-weight model with a license that fits your use: Llama, Mistral, Mixtral, Qwen, Gemma, Phi, DeepSeek and domain models. We also help you check license terms.
Do we need to buy GPUs first?
No. We size the workload first. Many teams start on rented or reserved GPUs in a private cloud and move to owned hardware once usage is proven.
Often combined with.
GPU Infrastructure Optimization
Cut inference and training cost by raising utilization across your GPU fleet.
Learn moreCloud-to-AI Migration
Move legacy workloads and data onto AI-ready infrastructure without downtime.
Learn moreMLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring end to end.
Learn moreReady to run AI on your own terms?
Book a 30-minute consultation with an infrastructure engineer. You'll leave with a clear view of options, costs and next steps, whether or not you work with us.