Local AI DeploymentYour Data, Your Control

Hardware requirements depend on the model size. For smaller models (7B-13B parameters), a single NVIDIA RTX 4090 or A6000 may suffice. For larger models (70B+), you'll need multiple A100 GPUs or equivalent. We'll assess your requirements and recommend the optimal hardware configuration during consultation.

With local deployment, you pay a fixed monthly fee regardless of usage. For organizations processing millions of tokens monthly, this typically results in 70-90% cost savings compared to pay-per-token APIs. The break-even point is usually around 10-50 million tokens per month, depending on the model.

Yes! One of the biggest advantages of local deployment is the ability to fine-tune open-source models on your proprietary data. This creates domain-specific AI that understands your business context, terminology, and requirements better than generic models.

We handle all model updates, security patches, and infrastructure maintenance. For dedicated server deployments, this is included in your monthly fee. For on-premise deployments, we offer maintenance contracts or can train your team to manage updates independently.

With on-premise deployment, your data never leaves your infrastructure. For dedicated servers, we provide isolated environments with encryption at rest and in transit, private networking options, and compliance with GDPR, HIPAA, SOC2, and other regulations.

We can deploy any open-source model including Llama 3.1, Mistral, Qwen, DeepSeek, Phi-3, Gemma, CodeLlama, Stable Diffusion, and many more. We can also help you evaluate and select the best model for your specific use case.

Dedicated server deployment typically takes 1-2 weeks from contract signing. On-premise deployment depends on your infrastructure readiness but usually takes 2-4 weeks including hardware setup, model deployment, and testing.