Dedicated AI Inference Hosting GPU Servers for Production AI
Dedicated GPU infrastructure with predictable performance, granular security controls, and SLA-backed reliability. No shared instances, no noisy neighbors, no unpredictable latency. vLLM-optimized serving on NVIDIA RTX PRO 6000, A100, and H100 hardware.
Dedicated vs. Cloud GPU Instances
Consistent performance, predictable costs, and security controls that shared infrastructure cannot provide.
Performance Advantages
- Zero latency variability. Cloud instances vary 200-400% between invocations.
- Guaranteed VRAM allocation with no GPU time-slicing or resource contention
- vLLM with continuous batching, PagedAttention, and tensor parallelism tuned for your model
Cost Advantages
- Fixed monthly pricing. No per-token charges, no egress fees, no surprise bills.
- Cloud H100 costs $17,000-$35,000/year per GPU at 24/7 utilization
- Scale usage up without scaling costs proportionally
What We Deploy
End-to-end inference infrastructure managed by our engineering team.
Dedicated GPU Servers
Single-tenant servers from RTX 5090 to multi-GPU EPYC configurations with 288 GB+ VRAM. No multi-tenancy, no resource contention.
vLLM Production Deployment
Continuous batching, PagedAttention, tensor parallelism, and quantization configured for your model. OpenAI-compatible API endpoints included.
API Gateway & Load Balancing
Authentication, rate limiting, request routing, and health-check-based failover across multiple GPU servers. Per-client usage tracking and quotas.
Monitoring & Observability
Prometheus and Grafana dashboards for GPU utilization, latency percentiles, throughput, and error rates. Automated alerting included at no extra cost.
Security Hardening
Network segmentation, encrypted storage, TLS 1.3, firewall rules, intrusion detection, and audit logging. CMMC and HIPAA controls available.
Managed Operations
24/7 monitoring, hardware maintenance, OS patching, model deployment, performance optimization, and SLA-backed uptime guarantees.
How It Works
Workload assessment: model size, latency, throughput, compliance
Hardware sizing and benchmark against your specific model
Server provisioning with security hardening
Model deployment with vLLM optimization
API endpoint configuration and load testing
Go-live with monitoring, alerting, and SLA
Frequently Asked Questions
What models can you host?
Any open-source model, fine-tuned variant, or custom architecture. We deploy Llama, Mistral, Qwen, and custom models. Quantization reduces memory requirements while maintaining output quality. Multi-model serving runs different models on the same infrastructure.
What uptime SLA do you offer?
We maintain 99.9%+ uptime across our infrastructure with defined response times for different severity levels. Scheduled maintenance windows with advance notification. Disaster recovery procedures with documented RTO and RPO targets.
Can I migrate from cloud AI providers easily?
Yes. Our OpenAI-compatible API endpoints let you switch from cloud AI providers to dedicated infrastructure with minimal code changes. We handle the migration of model weights, API configuration, and performance tuning.
Is this compliant for healthcare and defense workloads?
Yes. Network segmentation, encrypted storage, access controls, and audit logging satisfy HIPAA and CMMC requirements. Our private AI solutions detail architecture for the most security-sensitive deployments.
How does pricing work?
Fixed monthly pricing based on your server configuration. No per-token charges, no egress fees, no usage-based escalation. You know exactly what your AI infrastructure costs every month regardless of query volume.
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Ready for Dedicated AI Inference?
Get a custom hosting proposal with performance benchmarks for your specific model.