Question 1

What is local LLM deployment?

Accepted Answer

Local LLM deployment runs large language models on your own infrastructure—on-premises servers or private cloud—rather than sending data to third-party AI services. Your data never leaves your control. This is essential for sensitive data, regulated industries, and organizations that need to maintain data sovereignty. You get the power of modern AI while keeping complete control of your information.

Question 2

What is LLM fine-tuning?

Accepted Answer

Fine-tuning adapts a pre-trained language model to your specific domain, terminology, and use cases. A general model might not understand your industry jargon or specific processes. Fine-tuning trains the model on your data—documents, conversations, procedures—so it responds with higher accuracy for your specific needs. The result is AI that 'speaks your language' and understands your domain.

Question 3

How much does local LLM deployment cost?

Accepted Answer

Costs include infrastructure and development. Infrastructure varies based on model size: smaller models (7B parameters) can run on modest hardware ($5K-$15K). Larger models need more powerful GPUs ($20K-$100K+). Development for fine-tuning and deployment typically ranges $50,000-$150,000. Ongoing costs are lower than API-based AI at scale since you're not paying per-token. We help right-size for your needs.

Question 4

What hardware do we need?

Accepted Answer

Hardware depends on model size and usage. Smaller models (7B-13B parameters) run on single GPUs like NVIDIA RTX 4090 or A10. Larger models (70B+) need multi-GPU setups with A100 or H100 GPUs. We help size infrastructure for your use case—often starting smaller and scaling as needs prove out. Cloud GPU instances are an option if you don't want to buy hardware.

Question 5

Which LLM models do you work with?

Accepted Answer

We work with leading open-source models: Llama (Meta), Mistral, Mixtral, Phi (Microsoft), Qwen, and others. Model selection depends on your use case—some excel at conversation, others at code or analysis. We evaluate options and recommend the best fit. Open models can be fine-tuned and deployed privately; proprietary models like GPT-4 cannot.

Question 6

How does fine-tuning improve results?

Accepted Answer

Fine-tuning improves accuracy for your specific domain. A general model might not know your product names, internal processes, or industry terminology. After fine-tuning on your data, the model understands your context, uses appropriate terminology, and provides more accurate responses. Improvements of 20-50% in task accuracy are common compared to generic models.

Question 7

What data do you need for fine-tuning?

Accepted Answer

Fine-tuning data depends on your use case. Common sources include: documents, manuals, and knowledge bases; conversation logs and support tickets; internal wikis and procedures; examples of good responses or outputs. We help prepare and curate training data. Quality matters more than quantity—hundreds of good examples often outperform thousands of poor ones.

Question 8

How long does deployment take?

Accepted Answer

Initial deployment of an off-the-shelf model can happen in 2-4 weeks. Fine-tuning adds 4-8 weeks depending on data preparation and iteration. Full production deployment with integration, testing, and refinement typically takes 2-4 months. We deliver incrementally—you see working AI early while we optimize for production quality.

Local LLM Fine-Tuning & Private Deployment

What You Get Private LLM Services

On-Premises Deployment

Your Infrastructure

Private Cloud

Isolated Environment

Fine-Tuning

Domain Training

Model Selection

Right-Sized AI

Infrastructure Design

Hardware Planning

Inference Optimization

Fast Responses

API Integration

Easy Access

Monitoring

Performance Tracking

Ongoing Support

Updates & Maintenance

Private AI That Speaks Your LanguageFine-tuned models on your infrastructure.

Data Sovereignty

Complete control

Domain Accuracy

Fine-tuned for you

Cost Control

No per-token fees

LLAMA

META'S OPEN LLM

MISTRAL

EFFICIENT PERFORMANCE

PHI

MICROSOFT MODELS

MIXTRAL

MIXTURE OF EXPERTS

What You Get with Private LLM Deployment

Enterprise Security

Air-gapped option

Custom Training

Your data, your model

Standard APIs

Easy integration

Frequently Asked Questions Common Questions About Private LLM

Deployment

Models & Data

MainSail Data deploys private AI on your infrastructure.