In the ever-evolving landscape of Artificial Intelligence (AI) and Generative AI (GenAI) the term 'inference server' is increasingly becoming a must-know concept among ML engineering leaders. But what exactly is an inference server, and why is it crucial in the world of generative AI applications?
What is inference?
To appreciate the role of an inference server, it's essential to understand the concept of 'inference' in AI. In the AI lifecycle, there are two main phases: training and inference. During training, a model learns from vast amounts of data, identifying patterns and gaining knowledge. Inference, on the other hand, is the application of this model to make predictions or decisions.
For enterprises, especially when working with pre-trained transformer models, inference is the most important stage of the AI model lifecycle, this is where the majority of compute spend occurs and it is the point at which the model actually starts delivering value!
Inference server: The AI workhorse
Inference servers are the “workhorse” of AI applications, they are the bridge between the trained AI model and real-world, useful applications. Inference servers are specialised software that efficiently manages and executes these crucial inference tasks.
The inference server handles requests to process data, running the model, and returning results. An inference server is deployed on a single ‘node’ (GPU, or group of GPUs), it is scaled across nodes for elastic scale through integrations with orchestration tools like Kubernetes. Without an inference server, the model weights and architecture are useless, it is the inference server that gives us the ability to interact with the model and build it into our application.
Key characteristics of an effective inference server
An inference server when built well, can be a force multiplier for the scalability and efficiency of AI applications and of ML Engineering teams. Here are some key characteristics of an effective and scalable inference server appropriate for enterprise-scale applications.
Scalability: As demand fluctuates, an effective inference server should be able to deal with high demand. Best in class inference servers include optimisations which improve throughput as well as allowing the deployment of multiple models on a single GPU, ensuring consistent performance while maintaining efficiency.
Model and hardware support: In a rapidly moving industry, it is important that your inference server is able to support all popular models and hardware and has commitments to add new models and hardware as and when they are released.
Speed and Efficiency: In many applications, like real-time language translation, speed is critical. An inference server must deliver high-speed, efficient processing to meet these demands. An effective inference server will include model optimisations, this can lead to latency decreases of more than 10x.
Reliability: Consistent availability is non-negotiable, as many applications depend on continuous AI inference. This can only be achieved by working with an inference server that has been battle tested as comes alongside strong SLAs.
Cost Efficiency: An effective inference server should include optionality to compress your AI model so it can be deployed on cheaper more cost effective hardware, the current state of the art for this kind of compression is AWQ quantisation.
Controllers: Most enterprise Generative AI tasks benefit from controllers which can ensure the reliability of the outputs, a common controller is a JSON controller that can guarantee the outputs to fit with a defined JSON schema.
Integrations and Framework Support: Building and deploying Generative AI applications is difficult, your inference server should include integrations with frameworks and systems to reduce development time. Common Integrations are with CI/CD pipelines, monitoring tools, orchestration frameworks, and vector databases.
Interoperability and Portability: High performance inference servers should support the ability to deploy multiple applications and models to a single inference engine. This allows for interoperability and portability of models and applications and leads to efficiencies when deploying multiple applications.
Elastic Deployment: Serving a language model on a single GPU or machine is fine for demos and early applications, but when you go out to production you want a system that can scale to match the demands your users place upon it. Any inference solution needs to be easily integrated into elastic ML serving tools like Kubernetes, Seldon, or similar.
Monitoring and Logging: High performance Inference Servers should include integrations for monitoring performance and logging activities for audit and improvement purposes.
Which inference server is right for me?
An inference server is necessary whenever you want to inference an AI model. However, if using API based model providers like OpenAI, the inference server is managed by the model provider and is abstracted away from the application developers.
However, many businesses are deciding to self-host their models rather than using API based models because of privacy, security, scalability, and cost factors. Given that you are self-hosting your Generative AI application then it is essential that the choice of inference server is taken carefully.
Titan Takeoff Inference Server is the inference server of choice for businesses looking to build and deploy Generative AI applications in their secure environment. Titan Takeoff is a battle tested enterprise-grade inference server which allows users to scale with confidence, minimise inference costs by 90%, and significantly improve developer velocity.
Reach out to firstname.lastname@example.org if you would like to learn more and find out if the Titan Takeoff Inference Server is right for your Generative AI application.