Get the latest tech news
Here are 3 critical LLM compression strategies to supercharge AI performance
How techniques like model pruning, quantization and knowledge distillation can optimize LLMs for faster, cheaper predictions.
As these models are deployed in real-time applications, like recommendation engines or threat detection systems, their need for high-performance GPUs or cloud infrastructure drives up costs. In cases where the performance loss is still more than acceptable, techniques like quantization-aware training can help maintain accuracy by allowing the model to adapt to this compression during the learning process itself. By adopting these strategies, companies can reduce their reliance on expensive hardware, deploy models more widely across their services and ensure that AI remains an economically viable part of their operations.
Or read this on Venture Beat