Private AI Deployment

Private & On-Premise GenAI Deployment Services

We host open-weight models inside your corporate virtual clouds or on-premise GPU systems to secure data residency.

Zero
Data Leakage

No text data ever leaves your private network or server cluster.

4-Bit
Quantization

Dynamic memory scaling allowing models to run on affordable cards.

vLLM
Serving Core

PagedAttention technology maximizing inference concurrency rates.

Air-gapped
Hosting

Complete deployment isolation for high-security environments.

Capabilities

Production-Grade Private AI Deployment Capabilities

dns

Local Inference Engines

Hosting models like Llama or Mixtral locally using vLLM on enterprise GPUs.

shield

Data Residency Isolation

Ensuring all processing occurs within air-gapped private virtual clouds.

memory

GPU Cluster Optimization

Configuring dynamic batching and weights quantization to cut hosting costs.

Execution

How We Ship Production Pipelines

01

Cluster Sizing & Audit

We profile your concurrency demands to outline target GPU memory requirements.

02

Private Cloud Provisioning

We deploy vLLM or NIM containers inside AWS VPC, Azure, or local K8s.

03

Quantization & Tuning

We apply 4-bit or 8-bit weight quantization to optimize local performance.

04

Network Lock Down

We block external internet access, ensuring all text data remains within VPC boundaries.

We configure private GPU clusters and virtual clouds to deploy open-weight models with total security.

FAQs

Frequently Asked Questions

Can we run LLMs locally? expand_more
Yes, we host open-weight models on private GPU servers to guarantee complete data residency.
What hardware is required? expand_more
Required hardware depends on model size; typically one or more NVIDIA H100 or A100 GPUs.
edit Written by Umar Abbas (Principal AI Architect)
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