Enterprise LLMs and the Best Way to Deploy Them

Explore the transformative world of Large Language Models (LLMs) in our latest blog, where we delve into the challenges and triumphs of integrating these advanced AI tools into enterprise operations.

In an era where digital transformation is not just a buzzword but a business imperative, enterprises are rapidly adopting advanced technologies to stay ahead. Among these, Large Language Models (LLMs) have emerged as a game-changer, offering unprecedented opportunities for innovation and efficiency. However, seamlessly integrating these powerful tools into existing processes remains a formidable challenge, especially in the deployment phase.

This is where Mystic AI steps in. As a leading infrastructure-as-a-service provider for machine learning, Mystic AI is uniquely positioned to bridge the gap between the potential of LLMs and their practical, scalable deployment in enterprise environments.

In this blog, we will explore the rise of LLMs in business, the hurdles encountered during deployment, and how Mystic AI's tailored solutions are revolutionizing this space.

Open Source vs Close Models

One of the primary challenges enterprises face when adopting LLMs is the decision between open-source and closed-source models. This choice is crucial and is influenced by various factors including the company's specific needs, preferences, and budget constraints.

On one hand, open-source models like Llama2 and Mistral-7b offer flexibility and customizability. They allow companies to tailor the models to their specific use cases and often come with a lower upfront cost. However, these advantages are often accompanied by challenges in terms of support, maintenance, and the need for specialized expertise to deploy and manage them effectively.

On the other, closed-source models like OpenAI’s GPT-4 provide a more streamlined, user-friendly experience with robust support and regular updates. These models are typically easier to integrate and come with the assurance of continuous improvements and optimizations. However, they can be more expensive and offer less flexibility in terms of customization. Recognizing the potential of open-source models, and private models, Mystic AI offers a solution that simplifies their deployment. Our platform streamlines the process, reducing the complexities traditionally associated with open-source models. With Mystic AI, enterprises can leverage the advantages of open-source LLMs without the usual hurdles, making deployment as straightforward as it is with closed-source alternatives.

Explore Llama2, Mistral-7b, and more...

Finding the right hardware, Auto Scaling, Cold Starts

Deploying Large Language Models (LLMs) like Llama2 and Mistral-7b is an intensive process, especially considering their substantial resource requirements. These models, emblematic of the latest advancements in AI, demand robust computational power to function optimally.

Llama2 70B half-precision (16fp), for instance, requires approximately 150GB of GPU RAM and 2 A100-80GB GPUs for fast and efficient operation. Similarly, Mistral-7b needs 24GB of GPU RAM for a good throughput. This immense resource demand often leads to 'cold starts', a challenge where initiating these models from an inactive state can be time-consuming and resource-intensive. At Mystic, we address the issue through two key elements:

Autoscaling: To manage fluctuations in traffic, Mystic AI dynamically adjusts the number of deployed compute resources. This not only reduces costs but also maintains optimal response times. Our system calculates the ideal number of resources based on traffic patterns, historical data, and projected demands. This is crucial for both cloud-based and on-prem deployments, ensuring efficient resource utilization without compromising performance.

Preemptive Caching - Cold Start Optimization: We utilize concurrency metrics to pre-load pipelines on compute resources, anticipating demand. This approach reduces the impact of cold starts, especially when the time to initiate a pipeline is significant compared to its runtime. This ensures that incoming requests are processed swiftly, even during sudden spikes in traffic.

Cost Reductions with Spot Instances: By leveraging spot instances through our proprietary algorithm, Mystic AI significantly lowers operational costs. Our system intelligently scales these machines based on expected traffic, ensuring cost-effective, yet reliable, resource availability.

GPU Sharing and Environment Management: We streamline environment management and GPU fractionalization, crucial for teams working with multiple ML/AI models. Utilizing advanced features like NVIDIA's MIG, we enable efficient allocation of GPU resources, avoiding the dedication of entire GPUs to single models. This approach not only optimizes resource usage but also accelerates time-to-production and eases collaboration within infrastructure teams.

Through these techniques, Mystic AI ensures that the deployment of LLMs is not only efficient but also cost-effective, allowing businesses to leverage the power of AI without the burden of complex resource management.

Security and Compliance

As enterprises delve deeper into the integration of Large Language Models, the focus inevitably shifts to aspects of security, compliance, and deployment flexibility. This is where the choice of hosting becomes crucial, be it cloud, hybrid, or on-premises solutions.

At Mystic, we realise these varied needs, our Enterprise-grade ML deployment platform: Pipeline Core, is our solution….

Whether your preference leans towards prominent cloud providers like AWS, Azure, or GCP, or if a more tailored hybrid or on-premises setup suits your organizational needs, Mystic AI's Pipeline Core seamlessly aligns with your infrastructure.

One of our differentiators lies in our expertise with on-premises deployments. Since 2019, we have been managing our own on-premises setup at our data centers in Bath, England, ensuring top-tier security and compliance standards.

This proficiency in on-premises deployment isn't just about having control over physical servers. It's about offering a solution that's as robust and reliable as it is compliant and secure. With Mystic AI's Pipeline Core, businesses gain the flexibility to choose their deployment model without compromising on the efficacy and efficiency of their AI operations.

To summarize, Mystic AI simplifies the tough parts of using enterprise LLMs, with:

Flexible Model Deployment: Balances open-source and closed-source models, offering customization alongside ease of integration.
Efficient Resource Management: Addresses the challenges of 'cold starts' and high resource demands through autoscaling and preemptive caching.
Adaptable Hosting Solutions: Provides secure and compliant hosting options, including cloud, hybrid, and specialized on-premises deployments, tailored to diverse enterprise needs
Expertise in On-Premises Setups: Leverages years of experience since 2019 in managing robust and secure on-premises data centers in Bath, ensuring high standards of security and compliance.

Through these strategic solutions, Mystic AI ensures that the integration of LLMs into enterprise operations is not just possible, but also streamlined, stable, secure, and cost-effective.

Mystic AI essentially serves as your in-house MLOps team, skillfully navigating the complexities of LLM deployment while tightly controlling costs, providing unmatched performance, and reducing the overall complexity of navigating the rough seas of Enterprise AI.