Large Language Model Inference Efficiency: A Concern in the Face of Rapid Deployment

LLMs have made tremendous progress in quality and accuracy, leading to their adoption across various industries [15][43]. Most modern LLMs are based on transformers [45][46] and share similar characteristics [36]. However, these models are large and require expensive GPUs for training [14]. The recent surge in LLM deployment has resulted in a global GPU capacity crunch [12].
II. Deployment and Cost
Most datacenters and machines are currently being used for inference tasks instead of training [31][35]. This is because the number of use-cases that leverage LLMs is vast, and amortizing the high training costs through a large number of inferences is the most efficient way to do so [4]. While training these models is expensive and requires dedicated supercomputers [31][35], a large number of inferences can help offset these costs.
III. Inference Tasks
Inference jobs, although smaller than training tasks, are still costly due to the power requirements of GPUs [14]. This highlights the need for efficient deployment and use of datacenters and machines for inference tasks. By leveraging LLMs for a wide range of applications, we can amortize the high training costs and make these models more accessible to various industries.
In conclusion, the recent advancements in LLMs have led to their widespread adoption, but this has also created a global GPU capacity crunch. To address this challenge, it’s essential to develop efficient deployment strategies for inference tasks and make these models more accessible to various industries. By doing so, we can harness the full potential of LLMs and unlock their benefits across different sectors.