Local LLM Deployment for a Marketing Agency
Challenges
Recognizing the transformative potential of AI, the client was in search of a self-hosted AI chatbot to streamline the workflow and collaboration of their marketing teams.
The main challenge was to develop a chatbot embedded with a personal LLM (Large Language Model) that could be hosted on the client's internal infrastructure. The solution needed to prioritize the security of internal information, ensuring that data remained within the company’s private network.
Having successfully collaborated on the development of an internal marketing automation platform, the client was eager to leverage our proven expertise once again. Relying on Silk Data, as an AI development company, the client addressed our experts to run LLM locally.
About the project
Our client, a European digital marketing agency, assists companies in developing content and communication strategies in diverse fields such as fintech, IT, and beyond.
Region
Europe
Time
Ongoing
Team
6
Solution
To address these challenges, Silk Data assembled a dedicated team of six AI experts.
The initial development phase for the LLM chatbot demo version spanned 22 business days. The team opted for Mistral, an open-source LLM, as the foundation for their solution.
The initial attempt to run an on prem LLM on an Azure server was found to be too expensive for the client, needing a more cost-effective solution without compromising performance. After analyzing diverse options, the team decided to transition private LLM model to Hetzner server, which provided an optimal balance of cost and performance. This setup allowed the self-hosted LLM to run efficiently with a distribution of 70% on GPU and 30% on CPU, significantly reducing expenses while keeping high performance.
Key Development Stages
LLM Installation
The team deployed the Mistral LLM on the Hetzner server, ensuring compatibility with the chosen hardware configuration.Architecture Development
The team built the user interface (frontend) for the chatbot and the backend system responsible for communicating with the private LLM.LLM Model Testing
AI engineers meticulously tested the self hosted LLM model by providing it with specific contexts and evaluating its responses to ensure accuracy and relevance. This comprehensive testing ensured the chatbot could conduct basic conversations fluently in both German and English.
Key Outcomes
Just within one month, Silk Data has successfully developed, trained, implemented and tested the demo version of AI-powered chatbot.
Silk Data successfully deployed a local LLM model on the client's internal infrastructure. This solution addressed the client’s concerns about data security by ensuring that all information remained within their private network. Unlock the power of conversational AI! Build your own LLM or fine-tune ChatGPT for business needs with Silk Data’s 10 years of AI expertise.
Why Run a LLM Locally
Enhanced Data Security and Privacy
Deploying LLMs in a private cloud or corporate data center ensures that sensitive data remains within the organization’s control. This mitigates risks associated with data breaches and leaks, as the data is not exposed to third-party cloud providers. Compliance with regulations like GDPR, HIPAA, and CCPA is more manageable, as organizations can implement and monitor their stringent security measures.
Customization and Control
Organizations have full control over the model’s configuration, allowing for extensive customization. This includes tailoring the LLM to specific industry jargon, internal policies, and unique business processes, which can significantly improve the model’s relevance and accuracy in its responses.
Reduced Latency and Increased Performance
LLM hosting on local infrastructure can reduce latency, providing faster response times compared to remote cloud services. This is crucial for real-time applications, such as customer support LLM chatbots, where quick responses are essential.
Cost Management
Although the initial hardware cost might be high, running LLMs on-premises or in a private cloud over time can be more cost-effective than paying for extensive usage of third-party cloud services.
Local LLM Deployment: Pitfalls
- Incompatibility of multiple LLM models
When integrating multiple models, incompatibility issues often arise due to differences in architecture, data formats, and operational requirements. Each model may have been developed with distinct frameworks, assumptions, and dependencies, leading to challenges in seamless integration.
- Scalability Challenges
While private clouds and local data centers offer control and security, scaling the infrastructure to accommodate the ever-growing computational needs of LLMs can be challenging. Unlike public cloud services, which can dynamically allocate resources based on demand, private infrastructures may face limitations in scaling quickly and efficiently, especially during peak LLM install periods.
- Reduced accuracy of LLM model
Large models are typically designed to leverage extensive computational resources and distributed computing environments. When these models are adapted for local hardware, compromises must be made to fit the model within available memory and processing power constraints. Techniques such as model quantization can reduce the model size and improve efficiency, but they often come at the cost of reduced accuracy and generalization ability. The complexity of the original model's architecture may not fully translate to a downsized version, resulting in diminished performance. Balancing the trade-offs between model efficiency and performance is a critical and ongoing challenge in running LLM locally.
- Security Risks
Despite the increased control, private environments are not immune to security risks. Internal threats, such as malicious insiders or inadequate security practices, can pose significant risks. Additionally, ensuring the infrastructure is protected against external threats requires robust security measures, continuous monitoring, and regular updates.
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Frequently Asked Questions
Let's break down these concepts: NLP (Natural Language Processing) is a field of AI focused on enabling computers to understand, interpret, and generate human language. LLM (Large Language Models) are advanced AI models trained on vast amounts of text data to perform various language-related tasks. GPT (Generative Pre-trained Transformer) is a specific type of LLM developed by OpenAI, known for its ability to generate coherent and contextually relevant text based on input prompts. In essence, NLP is the broader field, while LLM and GPT are specific implementations within that field.
Building your own LLMs is expensive primarily due to the massive computational resources required for training on vast amounts of data and fine-tuning for specific tasks. Training these models involves running complex algorithms over millions or even billions of text samples, requiring extensive computational power, including high-end GPUs. Additionally, the cost of storing and accessing the enormous datasets used for training contributes to the overall expense. Moreover, the expertise and labor needed to develop and fine-tune these models further add to their cost. All these factors combine to make LLMs a substantial investment for companies and organizations.
Yes, it is possible to train LLM with your own data. However, be prepared for a challenging journey. Training private LLMs require expertise in machine learning, access to significant computational resources (powerful computers), and large datasets. Training can take days, weeks, or even longer depending on the model size and complexity. Your homemade LLM might not perform as well as state-of-the-art models trained on massive datasets by large companies. Therefore, an alternative approach to consider is "fine-tuning" a pre-existing model to align with your specific requirements. This method is often simpler and can yield effective results.
