Your AI. Your Hardware. Your Control.
Local LLM deployment for businesses that need to keep everything in-house
Data Sovereignty & Independence
When cloud APIs aren't an option, on-premise AI gives you complete control
Complete Privacy
Data never leaves your network. Process sensitive information without external exposure.
Zero Dependencies
No reliance on external LLM providers. Your AI keeps running even when OpenAI goes down.
Cost Control
Predictable hardware costs. No per-token fees. At scale, on-premise wins economically.
Compliance Ready
Meet regulatory requirements -- HIPAA, SOC2, GDPR, and industry-specific mandates.
From Planning to Production
Our structured approach ensures successful deployment
Requirements Analysis
We understand your use cases, volume expectations, latency requirements, and compliance needs.
Hardware Specifications
Detailed recommendations for GPUs, CPUs, memory, and storage based on your workload.
Performance Projections
Token rates, latency estimates, throughput calculations, and capacity planning.
Network Architecture
Load balancing, failover strategies, monitoring setup, and security configuration.
Installation & Config
Model deployment, optimization, testing, and validation of production readiness.
Training & Handoff
Your team learns to manage, monitor, and maintain the system independently.
Complete Technical Specifications
Everything you need to make informed decisions and successful deployments
Deliverables
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Hardware Requirements
GPUs, CPUs, RAM, storage specifications
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Throughput Metrics
Expected tokens/second, latency ranges
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Cost-Benefit Analysis
On-premise vs cloud API comparison
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Model Recommendations
Llama, Mistral, Qwen, and specialized models
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Optimization Strategies
Quantization, batching, caching approaches
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Installation Documentation
Step-by-step deployment guides
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Network Diagrams
Architecture and security topology
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Monitoring Setup
Prometheus, Grafana, alerting configuration
Support Options
Consulting Only
Full specifications and documentation. Your team handles deployment.
Guided Deployment
We walk your team through installation with live support.
Full Service
We handle everything from hardware procurement to production validation.
Ongoing Support
Optional maintenance packages include:
- • Model updates and fine-tuning
- • Performance optimization
- • Security patches
- • Priority support access
Deep Infrastructure Knowledge
We've built our own on-premise AI infrastructure -- we know what works
Multi-GPU Configurations
NVIDIA RTX, A100, H100 setups. AMD ROCm options. Tensor parallelism across cards.
Model Quantization
4-bit, 8-bit, GGUF formats. Balance quality vs performance for your use case.
Inference Servers
vLLM, TGI, LocalAI, Ollama. We recommend the right server for your workload.
Load Balancing
HAProxy, nginx configurations. Failover strategies. Geographic distribution.
Monitoring Stack
Prometheus metrics, Grafana dashboards, custom alerts for GPU memory and latency.
Container Orchestration
Docker, Kubernetes deployments. GPU scheduling. Rolling updates without downtime.
Who We Work With
✓ Good Fit
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Small to mid-sized businesses
NOT enterprise-scale (those have internal teams)
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Data privacy requirements
Legal, healthcare, financial services
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Ready for AI independence
Understand the value of owning infrastructure
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Serious implementations
Not hobbyist setups or experiments
Budget Expectations
Hardware Costs
Typically $5,000 - $50,000 depending on scale and performance requirements.
Consulting Engagement
Starts at $2,500 for specifications and documentation.
Full Service Deployment
Custom quotes based on complexity. Includes hardware guidance, installation, and training.
Note: At high volumes, on-premise often pays for itself within 6-12 months compared to cloud API costs.
Schedule a Consultation
Let's discuss your requirements and determine if on-premise AI is the right fit for your organization.
Start the ConversationNo obligation. We'll tell you honestly if cloud APIs are a better fit.