Scalability and deployment considerations
When deploying LLMs, considering scalability and infrastructure is crucial to ensure that the system can handle increased workloads without performance degradation. In this section, we will take a detailed look into the aspects of scalability and infrastructure considerations.
Hardware and computational resources
Setting up hardware and computational resources for LLM deployment is complex. Let’s review them in detail in the following sections.
High-performance GPUs
GPUs, being the backbone of modern ML infrastructures due to their parallel processing capabilities, are ideal for the matrix and vector computations LLMs require.
When evaluating GPUs, consider the following:
- Core count and speed: A higher number of cores and faster clock speeds generally translate to better performance
- Memory bandwidth and capacity: Adequate memory is necessary to train large models, as it allows for larger batch sizes and faster...
