Custom build on-premise Large Language Model — Fine-tuning models on private business data

Understanding enterprise LLM needs

In 2024, we’re witnessing a significant shift in how businesses approach Large Language Models.

While ChatGPT, Claude and other public models grab headlines, enterprises are increasingly looking beyond these general-purpose solutions toward customized implementations that align with their specific needs and data privacy requirements.

Current market overview

Taking a look on the martket, it shows remarkable growth, with projections indicating an expansion from $1.59 billion in 2023 to $259.8 billion by 2030.

This isn’t just abstract growth – we’re seeing real adoption across industries. Currently, 67% of organizations have integrated LLMs into their workflows, yet interestingly, only 23% have deployed commercial models in production.

This gap between experimentation and deployment reveals both the potential and the challenges enterprises face.

North America leads this transformation, with the market expected to reach $105.5 billion by 2030.

What’s particularly telling is that five major LLM developers currently control 88.22% of market revenue – a concentration that’s driving many businesses to seek more independent, customizable solutions.

Data sources: iopex.ai, datanami.com, springsapps.com, Charts by UnfoldAI.

Why custom LLMs matter for business

The limitations of general-purpose LLMs become apparent when handling specialized business tasks.

Generic models achieve low accuracy when processing real business data. This drops even further for expert-level requests, often approaching zero accuracy for highly specialized queries.

Recent survey data reveals a striking trend in enterprise LLM adoption. Despite the hype around commercial LLM solutions, 77% of enterprises have no plans to implement them, while only 23% are currently using or planning to use commercial LLMs.

This stark divide underscores a critical realization: businesses need more specialized, controlled solutions rather than generic commercial offerings.

Take healthcare organizations, for instance – their custom models achieve 83.3% accuracy in diagnostic assistance by analyzing historical patient data and similar cases.

This dramatic improvement over generic models demonstrates why customization isn’t just an option – it’s becoming a necessity for serious business applications.

But of course, it is not only healthcare which can benefit from custom LLMs.

The privacy-performance trade-off

The main barrier to LLM adoption isn’t technical capability – it’s more about trust.

Businesses are understandably hesitant to share sensitive information with third-party models (who want to share all his financial data with OpenAI?).

This includes everything from financial records and medical data to proprietary business processes. The solution isn’t to avoid LLMs entirely, but to implement them in a way that maintains control over sensitive data while leveraging the power of AI.

The path forward isn’t about choosing between privacy and performance – it’s about finding the right architecture that delivers both.

Companies can start with open-source models like Llama 3, Qwen 2 or Mistral and customize them using private data, creating systems that understand their specific domain while keeping sensitive information secure.

Business applications of custom LLMs

Let’s explore real-world applications where custom LLMs deliver tangible business value.

Instead of theoretical possibilities, we’ll focus on implemented solutions that solve specific business challenges. These examples come from actual deployments and community experiences.

Content creation with brand voice

Generic LLMs often struggle with maintaining consistent brand voice and technical accuracy.

A custom LLM, trained on your organization’s documentation, marketing materials, and internal communications, can capture the unique writing style, terminology, and brand guidelines specific to your company.

This goes beyond simple templating – the model learns to generate new content that naturally reflects your organization’s voice while maintaining technical precision in your domain.

Intelligent customer support

Support ticket handling represents one of the most promising applications for custom LLMs.

Imagine a system trained on your support history, technical documentation, and product specifications. It can understand customer inquiries in the context of your specific products, recognize unique technical terms, and provide accurate, contextual responses.

The key advantage here isn’t just automation – it’s the ability to maintain consistency in support quality while scaling operations.

Secure knowledge management

For organizations handling sensitive information, custom LLMs offer a compelling solution for knowledge management.

Law firms, financial institutions, and healthcare providers can deploy models that understand their specific terminology and requirements while keeping all data within their controlled infrastructure.

The model becomes an intelligent assistant that can retrieve, summarize, and analyze information without ever exposing sensitive data to external systems.

Technical documentation assistant

Software companies and technical organizations can benefit from custom LLMs trained on their codebase, documentation, and internal knowledge bases.

The model becomes proficient in company-specific architectures, coding standards, and technical approaches. This is particularly valuable for maintaining consistency across large development teams and accelerating onboarding of new team members.

Specialized data processing

One of the most compelling use cases comes from organizations handling domain-specific data.

As shared by a developer on Reddit, healthcare organizations are using custom LLMs to process patient interviews and automatically redact personal information, maintaining HIPAA compliance while reducing costly manual processing.

This exemplifies how custom models can handle specialized tasks that would be risky or impossible with generic services.

Internal communication enhancement

Custom LLMs can transform internal communications by understanding your organization’s structure, terminology, and processes.

The model can help draft departmental communications, standardize reporting formats, and ensure consistency across different teams – all while maintaining your organization’s specific communication patterns and requirements.

Regulatory compliance support

For regulated industries, custom LLMs offer unique advantages in compliance management.

By training on your specific regulatory requirements, internal policies, and compliance history, the model can assist in ensuring communications and documents align with necessary standards.

This is particularly valuable in financial services, healthcare, and legal sectors where compliance requirements are complex and specific.

Why customization matters

The power of custom LLMs lies in their ability to understand and operate within your specific context.

Unlike generic models that provide broad, general-purpose capabilities, custom LLMs offer:

• Complete data privacy and control;
• Deep understanding of your domain-specific terminology;
• Alignment with your organization’s voice and standards;
• Integration with your existing workflows and systems.

The trend is clear – organizations are moving beyond generic AI solutions toward specialized systems that understand their unique needs.

This shift isn’t just about improving efficiency; it’s about creating AI systems that truly understand and operate within your business context.

Technical implementation approaches

When implementing a custom LLM, data preparation becomes the cornerstone of success.

Let’s explore the technical process of transforming your business data into a high-quality training dataset.

Data preparation process

The process begins with your company’s raw business data – documents, conversations, support tickets, and internal communications.

This data needs to be structured in a specific format that LLMs can understand. The preparation process involves cleaning, formatting, and organizing your data while preserving its essential characteristics and domain-specific elements.

Synthetic data generation

One of the most innovative approaches to enhance your training dataset is synthetic data generation.

Using state-of-the-art LLMs (Sonnet 3.5 and OpenAI o1), we can create additional training examples that mirror your business patterns and terminology. This process helps address data scarcity issues while maintaining privacy – instead of using sensitive customer data, it is possible to generate similar but artificial examples that capture the same patterns and relationships.

Private dataset creation

The final stage combines your prepared business data with synthetic examples to create a comprehensive private dataset.

This dataset becomes the foundation for fine-tuning your custom LLM.

The key here is maintaining a balance between real and synthetic data while ensuring all examples align with your business requirements and use cases.

Quality assurance

Throughout this pipeline, each stage includes validation steps to ensure data quality:

• Checking for data consistency and accuracy;
• Validating synthetic data against business rules;
• Ensuring privacy requirements are met;
• Verifying domain-specific terminology usage.

This systematic approach to data preparation enables the creation of custom LLMs that truly understand your business context while maintaining data security and quality standards.

Infrastructure requirements

Setting up the right infrastructure for your custom LLM is crucial for both performance and security.

Modern deployment options have evolved far beyond the traditional choice between on-premise and cloud solutions.

Deployment landscape

The infrastructure diagram illustrates an example how enterprise LLMs can be deployed while maintaining security and performance.

At its core, the system consists of distributed servers hosting the LLM, protected by custom rules and policies, and accessed through secure APIs. This architecture ensures both scalability and controlled access.

Today’s “on-premise” solution doesn’t necessarily mean physical servers in your building. Instead, organizations can leverage dedicated servers in their region, ensuring data sovereignty while maintaining high performance.

This approach has transformed how businesses think about private LLM deployment.

Here’s how different deployment options compare:

Feature	Regional dedicated	Private cloud	Hybrid
Data control	Complete control, fixed location	High control, flexible location	Mixed control
Performance	Consistent, low latency	Variable, region-dependent	Location-dependent
Scalability	Hardware-limited	Highly scalable	Flexible
Cost structure	Fixed + maintenance	Usage-based	Mixed
Security	Custom security stack	Cloud provider + custom	Layered
Maintenance	Self-managed	Provider-assisted	Mixed

Hardware considerations

The computing requirements for LLM deployment vary significantly between training and inference phases. Training or fine-tuning a model demands substantial computational power, typically requiring high-performance GPUs like NVIDIA A100s or H100s, paired with significant system memory and fast storage. This intensive phase shapes your initial infrastructure decisions.

However, once trained, inference can run on more modest hardware. A production environment might use less powerful GPUs like NVIDIA T4s or A10s, or even CPUs, making deployment more cost-effective. This difference between training and inference requirements creates opportunities for efficient resource allocation.

You can also refer to this articles for deeper understanding about hardware requirements for inference:

• GPU memory requirements for serving Large Language Models;
• How to run LLMs on CPU-based systems;
• Improving LLM inference speeds on CPUs with model quantization.

Scaling strategy

The infrastructure diagram shows just an example approach to scaling.

Your LLM system connects multiple servers through a coordinated network, managing load distribution automatically. This setup allows for handling of varying demand without service interruption.

During low-demand periods, some servers can hibernate, reducing costs while maintaining readiness for traffic spikes. The API layer manages access and monitors usage, providing valuable insights for capacity planning.

Security framework

Security in LLM infrastructure extends beyond basic access control.

As shown in the diagram above, the system implements multiple security layers. Custom rules and policies govern model behavior, while API controls manage access patterns.

Regular data backups, encryption, and access logging become integral parts of the infrastructure, not bolt-on additions. The system maintains detailed audit trails of all interactions, essential for both security and compliance purposes.

The key to successful LLM infrastructure lies in its flexibility and security. Whether choosing regional dedicated servers or a hybrid approach, your infrastructure should adapt to changing needs while maintaining strict security standards.

This balance between flexibility and control enables organizations to leverage custom LLMs effectively while protecting their sensitive data and operations.

Efficient resource management

A key cost optimization strategy for LLM deployment is implementing “cold starts” and scaling to zero.

Using modern formats like GGUF (GPT-Generated Unified Format), systems can efficiently load models on demand and completely hibernate when inactive.

While scaling to zero is possible with various model formats, GGUF’s optimization makes this process particularly efficient and straightforward.

This approach, available through platforms like Hugging Face Inference Endpoints, can dramatically reduce operational costs – you only pay for actual usage time, not for idle servers.

When a request comes in, the system wakes up, loads the optimized GGUF model quickly, and serves the request. This capability is particularly valuable for organizations with intermittent LLM usage patterns.

Below you can find a quick video for GGUF deployment on Hugging Face Inference Endpoints.

And recently, we have the option to use HuggingFace HUGS. This new service provide easy way build AI applications with open models hosted in your own infrastructure, with the following benefits:

• In your company infrastructure: Deploy open models within your own secure environment. Keep your data and models off the Internet.
• Zero-configuration deployment: HUGS reduces deployment time from weeks to minutes with zero-configuration setup, automatically optimizing the model and serving configuration for your NVIDIA, AMD GPU or AI accelerator.
• Hardware-optimized inference: Built on Hugging Face’s Text Generation Inference (TGI), HUGS is optimized for peak performance across different hardware setups.
• Hardware flexibility: Run HUGS on a variety of accelerators, including NVIDIA GPUs, AMD GPUs, with support for AWS Inferentia and Google TPUs coming soon.
• Model flexibility: HUGS is compatible with a wide selection of open-source models, ensuring flexibility and choice for your AI applications.
• Industry standard APIs: Deploy HUGS easily using Kubernetes with endpoints compatible with the OpenAI API, minimizing code changes.
• Enterprise distribution: HUGS is an enterprise distribution of Hugging Face open source technologies, offering long-term support, rigorous testing, and SOC2 compliance.
• Enterprise compliance: Minimizes compliance risks by including necessary licenses and terms of service.

How can you deploy and scale open-source AI securely on your infrastructure? Introducing HUGS—an optimized, zero-configuration inference service by @huggingface that simplifies and accelerates the development of AI applications with open models for companies. 🤗

What is HUGS?
💡… pic.twitter.com/iZnkWYqVeo

— Philipp Schmid (@_philschmid) October 23, 2024

Fine-tuning strategy

When deploying custom LLMs, organizations face a crucial decision: whether to fine-tune existing models or implement RAG (Retrieval Augmented Generation). This choice significantly impacts both performance and resource requirements.

Choosing between RAG and Fine-tuning

Recent market research shows interesting adoption patterns.

As shown in a recent survey, 32.4% of organizations plan to implement fine-tuning, while 27% opt for RLHF (Reinforcement Learning from Human Feedback). The significant portion of undecided respondents (40.6%) highlights the complexity of this decision.

When deciding between RAG and fine-tuning, consider these key characteristics and requirements:

Aspect	RAG	Fine-tuning
Data updates	Real-time updates without retraining.	Requires retraining for new information
Data volume	Handles large, dynamic datasets efficiently	Limited to training data size
Response time	Additional latency from retrieval	Faster, direct responses
Implementation	Quick setup, lower initial compute needs	Requires significant compute for training
Control	Precise control through document selection	Control through training data and parameters
Privacy	Depends on retrieval system security	Complete control over model and data
Use case focus	Current, factual information retrieval	Domain-specific language and style
Cost structure	Higher ongoing compute costs	Higher upfront costs, lower inference costs

For deeper insights into RAG implementation, explore these resources:

• Mastering RAG — How ReRanking revolutionizes information retrieval;
• Enhancing domain-specific RAG systems;
• Retrieval Augmented Generation (RAG) limitations;
• My book “Build RAG applications with Django”.

Implementation approaches

Modern LLM customization often combines both strategies. For instance, you might fine-tune a base model on your domain-specific terminology and writing style, then enhance it with RAG for accessing up-to-date information. This hybrid approach leverages the strengths of both methods while mitigating their individual limitations.

Open-source models

The choice of base model significantly impacts your custom LLM’s capabilities, resource requirements, and deployment costs. Let’s explore the current landscape of open-source models and how to select the right one for your use case.

Model selection framework

When choosing a base model, three key factors come into play: model size (parameters), model type (instruct vs chat), and licensing terms. The number of parameters directly affects both capabilities and resource requirements.

Model Size	Parameters	Use cases	Resource requirements (optimal)
Small (1-3B)	1-3 billion	Simple tasks, text generation, classification	Single GPU, 8-16GB VRAM
Medium (7-13B)	7-13 billion	General purpose, good balance	Single/Dual GPU, 24-32GB VRAM
Large (30-70B)	30-70 billion	Complex reasoning, specialized tasks	Multiple GPUs, 48GB+ VRAM
Extra Large (70B+)	70-405 billion	Research, high-complexity tasks	Multiple GPUs, 80GB+ VRAM

The Hugging Face Open LLM Leaderboard provides an essential resource for comparing model performance across different benchmarks and use cases.

Recommended models

In the table below you can find some good models to start with.

Model	Parameters
Qwen2-72B	72B
Llama-3.1-Nemotron-70B	70B
Llama-3.1-405B	405B
Qwen2.5-3B	3B
Phi-3.5-mini	3B
Gemma-2b	2B
Llama-3.2-1B	1B

The number of parameters directly correlates with model capabilities and resource requirements.

While larger models (70B+) excel at complex reasoning tasks, they demand significant computational resources – often multiple GPUs with substantial VRAM.

Models in the 7-14B parameter range strike a middle ground, providing strong performance on most tasks while being able to run on a single high-end GPU or even on CPU server with enought RAM (yes, a lot slower, and needs quantization + other performance optimizations).

Smaller models (1-3B) offer practical alternatives for many business applications, requiring just a single GPU while maintaining reasonable performance. They can also run on CPU, more about this is explained here.

Choosing model type

Modern LLMs come in different variants – instruction-tuned (“instruct”) or conversation-tuned (“chat”).

Instruct models excel at following specific directives, making them ideal for task-focused applications.

Chat models, optimized for dialogue, better handle context and maintain conversation flow.

Your choice should align with your primary use case.

Multimodal capabilities

Vision Language Models (VLMs) represent a significant advancement in multimodal AI.

These models process both text and images, enabling applications like document analysis, visual QA, and image-based reasoning. When considering VLMs, evaluate whether your use case truly requires visual processing capabilities, as these models typically demand more resources than text-only alternatives.

The key to successful model selection lies in balancing capabilities against practical constraints.

Consider your specific use case, available computing resources, and scaling requirements. Then validate the license terms align with your intended usage – some models permit unrestricted commercial use, while others require specific agreements or have usage limitations.

Practical implementation steps

Moving from model selection to actual implementation requires careful planning and realistic expectations.

Let’s focus on aspects we haven’t covered yet in our discussion of custom LLM deployment.

Implementation timeline

A typical implementation journey spans across three distinct phases.

The initial Proof of Concept phase usually takes 2-3 weeks, during which teams experiment with different models and validate their approach against specific business needs.

This is followed by the core development phase lasting 1-2 months, where the focus shifts to data preparation, fine-tuning, and system integration.

The final production rollout typically requires 2-4 weeks for deployment, monitoring setup, and user training.

Resource planning

Resource planning extends beyond the hardware considerations we discussed earlier. A successful implementation requires a balanced team composition.

While an ML engineer handles model development and a DevOps specialist manages infrastructure, the often-overlooked role of domain experts proves crucial for data validation and quality control.

These subject matter experts ensure the model learns from accurate, relevant information and maintains business-specific standards.

Optimization techniques

Model optimization through quantization and distillation can significantly reduce deployment costs.

Quantization, as mentioned earlier in short, converts model weights from 32-bit to 4-bit or 8-bit precision, dramatically reducing memory requirements while maintaining most of the performance.

Model distillation takes this further by creating a smaller, faster model that learns to mimic the behavior of a larger one.

These techniques can reduce hosting costs by 50-80% while maintaining acceptable performance for many business applications.

Measuring success

Success metrics for custom LLM implementations should go beyond standard machine learning metrics like accuracy or perplexity. The real measure of success lies in business impact.

User adoption rate shows how effectively teams integrate the model into their workflows.

Time savings on specific tasks provide concrete evidence of efficiency gains.

Error reduction rates, particularly in areas requiring human review, demonstrate quality improvements.

Perhaps most importantly, tracking cost per query helps compare the solution’s efficiency against previous systems or third-party APIs.

The path to successful implementation starts small but thinks big. Rather than attempting a company-wide rollout immediately, focus on a specific use case where success can be clearly measured and demonstrated.

This approach allows for careful validation of results and provides valuable insights for scaling the solution across other business areas.

Security and compliance

Security considerations should be at the forefront of any custom LLM implementation. Recent analysis shows that traditional security measures aren’t sufficient for LLM-specific threats and vulnerabilities.

Core security concerns

When deploying custom LLMs, three critical security risks demand attention: prompt injection, data exfiltration, and model manipulation. Prompt injection attacks can override system prompts and manipulate model behavior.

Data exfiltration risks are particularly concerning when LLMs process sensitive business data.

Model manipulation might lead to unintended behaviors or biased outputs that could harm business operations.

Data privacy framework

Custom LLM implementations require a comprehensive data privacy strategy. This includes:

1. Data processing controls – All data used for training and inference must be processed within designated secure environments. This is especially crucial when dealing with customer information, proprietary business data, or regulated information.
2. Data retention policies – Clear guidelines for how long data is stored, both for training datasets and model interactions. Implementation of automatic data purging mechanisms helps maintain compliance with data protection regulations.
3. Privacy-preserving techniques – Using advanced anonymization and pseudonymization methods when processing sensitive data. This ensures that even if a breach occurs, sensitive information remains protected.

Access control architecture

Access control for custom LLMs extends far beyond simple username and password protection.

A strong security architecture starts with comprehensive authentication mechanisms across all API endpoints and model access points.

These serve as the first line of defense against unauthorized access.

Building upon this foundation, role-based access control (RBAC) provides granular permissions based on user roles and specific use cases, ensuring users can only access the features and data necessary for their work.

Maintaining detailed audit trails becomes crucial for security oversight.

Every interaction with the model, from training sessions to inference requests, should be logged and monitored. This creates a transparent record of system usage and helps identify potential security incidents quickly.

Supporting this monitoring infrastructure, a strong API key management system with regular rotation policies ensures that even if credentials are compromised, the window of vulnerability remains limited.

Plagiarism prevention

Content plagiarism represents a significant concern when deploying LLMs for content generation.

Custom LLMs need robust mechanisms to ensure original content creation and prevent unintentional copying. This can be achieved through a multi-layered approach: implementing real-time plagiarism detection APIs during content generation, enforcing automatic content rewriting when similarity thresholds are exceeded, and utilizing synonym replacement techniques to maintain meaning while ensuring uniqueness.

Some organizations also implement “style mixing” – training models to combine multiple writing styles in ways that maintain readability while producing genuinely original content. For highly sensitive industries, implementing version control and content fingerprinting can provide an audit trail of generated content and its originality verification.

Regulatory alignment

Custom LLM deployments must align with various regulatory frameworks:

• GDPR compliance for processing European user data;
• HIPAA requirements when handling healthcare information;
• Industry-specific regulations like FINRA for financial services;
• Regional data sovereignty requirements.

The key to maintaining compliance lies in implementing proper documentation, regular audits, and having clear procedures for handling data subject requests.

Your LLM security strategy should evolve with emerging threats and changing regulatory landscapes. Regular security assessments and updates to security protocols ensure your custom LLM remains protected while delivering business value.

Next section will explore how maintaining full control over your LLM infrastructure ensures both security and operational flexibility.

Control

Maintaining complete control over your LLM infrastructure and data becomes increasingly crucial. While major providers offer powerful solutions, relying too heavily on third-party services can introduce risks and dependencies that may limit your organization’s flexibility and data sovereignty.

Recent developments, like Anthropic’s announcement of Claude’s ability to control computers directly, highlight both the impressive capabilities of modern LLMs and the importance of maintaining appropriate boundaries.

While such features demonstrate progress (even it is still in public beta and not fully working), they also underscore why organizations might prefer maintaining strict control over their AI systems, particularly when handling sensitive business data.

A self-controlled LLM infrastructure provides several critical advantages:

• Complete data sovereignty with all information remaining within your controlled environment;
• Freedom to modify and fine-tune models according to specific business needs;
• Independence from third-party pricing changes or service modifications;
• Ability to implement custom security measures and compliance protocols;
• Direct control over model behavior and output filtering.

The key to successful LLM deployment lies in finding the right balance between using existing technologies and maintaining control over critical components.

While using open-source models as starting points, organizations should retain full control over their training data, fine-tuning processes, and deployment infrastructure.

This approach ensures both technological advancement and operational independence.

Conclusion

The journey to custom LLM implementation represents a significant shift in how enterprises approach AI integration. Throughout this guide, we’ve explored why 77% of businesses are seeking alternatives to commercial LLM solutions, and how custom implementations can address the fundamental challenges of data privacy, performance, and control.

Whether through fine-tuning, RAG implementation, or a hybrid approach, organizations can leverage open-source models while maintaining complete control over their sensitive data and operations.

Remember that the path to success often starts small. Begin with a specific use case where impact can be clearly measured, whether it’s enhancing customer support, streamlining documentation, or automating internal processes. This focused approach allows for careful validation while building the expertise needed for broader deployment.

While the technical aspects of custom LLM deployment are complex, the business case is straightforward: organizations need AI solutions that understand their specific domain, respect their data privacy requirements, and remain under their direct control. The investment in custom LLM infrastructure pays dividends through improved accuracy, reduced dependencies, and enhanced security.

Looking ahead, the trend toward custom LLM solutions will likely accelerate as more organizations recognize the limitations of generic, third-party models.

The question isn’t whether to implement custom LLMs, but how to do so in a way that best serves your organization’s specific needs and objectives.