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Here are 3 critical LLM compression strategies to supercharge AI performance
- Businesses using AI face challenges including latency, memory usage and costs for running an AI model.
- Larger models are generally more accurate but require substantial computation and memory resources.
- Model compression techniques reduce the size and computational demands while still maintaining the model's performance.
- Model pruning removes certain parameters and speeds up the model's inference times and reduces memory usage.
- Quantization reduces precision of model's parameters and computations, leading to a significant drop in memory footprint and quicker inference speeds.
- Knowledge distillation involves training a smaller model to mimic a larger, more complex model.
- Adopting these strategies increases operational efficiency and makes AI a more economically viable part of operations.
- Companies can reduce reliance on expensive hardware, deploy models more widely and ensure AI remains a viable part of their operations.
- These strategies optimize ML inference for real-time AI solutions where speed and efficiency are critical.
- Small models perform fast and efficiently, providing users a seamless experience with practical and cost-effective AI solutions.
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