Over the last few years, we have witnessed the growth of large language models (LLMs) and Multimodal models in applications such as ChatGPT, DeepSeek, Claude, and more. These software-as-a-service (SaaS) applications have shown us the tangible benefits of AI models: they are accessible, easy to use, and valuable.
However, although developing, training, fine-tuning, and deploying AI models is complex, one of the biggest challenges is within inference - deploying models for real-world use. You need speed, scalability and cost-effectiveness whilst running on a diverse range of hardware architectures.
If you don’t optimise your inference, it can lead to high latency, high compute costs, and inefficient resource use.
The solution? AI frameworks. These provide the essential tools, libraries, and guidelines needed to build, optimise, and deploy AI models efficiently– helping developers ensure their models perform efficiently in production environments.
Focusing on customising an AI model is a developer's dream, not building it from scratch.
This article will explore AI frameworks, their functions, and how to choose the right one for Inference.
What is an AI Framework?
AI frameworks provide pre-built tools, libraries, and guidelines to simplify the process of creating, deploying, and managing AI models. Thus, developers can focus on customising, optimising, and deploying AI models in the real world.
When looking at AI frameworks in the inference process, key components include the model, input data, hardware, and the inference engine.
- The Model - The trained model needs to be prepared for inference to ensure it is optimised for the specific hardware type, such as GPUs, TPUs, etc. During this stage, the model’s computational demands must be optimised to align with the hardware capabilities, ensuring efficient processing and reduced latency during inference tasks.
- Input Data - Before the data can be analysed, it must be compatible with the model. For example, the range of data values must be adjusted to a standard scale, or categorical data must be encoded. This process ensures the maintenance of accuracy and efficiency in the model’s prediction.
- Hardware Compatibility - It is essential to understand what hardware is required for your AI workload, taking into consideration speed, efficiency, and cost. AI models can be run on CPUs, GPUs, TPUs, or dedicated accelerators, each offering different capabilities and characteristics. When choosing the right hardware, you should assess the model’s complexity, batch size, and power constraints.
- Inference Engine - The inference engine is an intermediary between the model and the underlying hardware. It optimizes inference workloads by efficiently utilizing hardware resources through model compression, managing request load balancing, and handling queuing mechanisms. This ensures maximum throughput and minimizes response time, enabling efficient execution of large language models.
- Scalability - Scalability is critical for AI workloads. Supporting deployment across cloud, edge, and on-premise environments ensures flexibility, performance, and cost efficiency.
Note that all AI frameworks serve the same purpose; however, some are designed specifically for training, whilst others are optimised for inference. Understanding the difference between training and inference is important in choosing the right AI framework for your needs.
Choosing the right AI framework for your needs
The AI framework you choose directly influences the efficiency and performance of your AI initiatives. Therefore, you must consider several factors, such as performance, ease of implementation, integration capabilities, flexibility and cost.
Performance
Let’s start with performance, as that seems to be the number one pain point when choosing the right AI framework for your AI workloads. Performance is the foundation of efficient inference. You want to see how effectively the framework can manage data and execute tasks. A well-optimised AI framework ensures low latency, high throughput and minimises resource consumption.
Cost
Cost is one of the biggest challenges when choosing an AI framework. Finding the right balance between affordability and performance is key. Choosing one that aligns with your budget can be difficult without compromising performance. However, it may be worth considering whether the framework can reduce costs in other areas, such as the need for additional hardware.
Another way to optimise your costs is by choosing cost optimisation strategies such as a ‘pay-as-you-go’ billing system. This way, you can scale your AI workloads efficiently whilst only being charged for the resources you use.
Flexibility
Flexibility in AI frameworks is essential in ensuring adaptability to different AI workloads. You want it to be able to test different types of algorithms and adapt to different data types, with ease. Therefore, consider if your choice of AI frameworks supports this based on the type of AI application you intend to develop.
Integration capabilities
You should ask yourself how easy it is to implement and integrate this AI framework with existing AI ecosystems. An easy-to-adopt AI framework saves organisations valuable resources, allowing them to focus on development. However, simply integrating your existing AI tools and infrastructure can help prevent compatibility issues and disruptions in your workflow.
How Nscale’s Serverless Inference supports AI frameworks
Nscale’s Serverless Inference platform supports all the latest open-source generative AI models, surfacing them using the OpenAI standard API. This compatibility makes it easy to switch your existing apps to Nscale Serverless Inference with just a few configuration lines. Moreover, the standard API between models empowers developers to test different models to find the one that best suits their needs.
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