In this blog post, we will go over the most effective techniques at the time of writing this blog post to tackle these challenges for efficient LLM deployment:

1. Lower Precision: Research has shown that operating at reduced numerical precision, namely 8-bit and 4-bit, can achieve computational advantages without a considerable decline in model performance.

2. Flash Attention: Flash Attention is a variation of the attention algorithm that not only provides a more memory-efficient approach but also realizes increased efficiency due to optimized GPU memory utilization.

3. Architectural Innovations: Considering that LLMs are always deployed in the same way during inference, namely autoregressive text generation with a long input context, specialized model architectures have been proposed that allow for more efficient inference. The most important advancement in model architectures hereby are Alibi, Rotary embeddings, Multi-Query Attention (MQA) and Grouped-Query-Attention (GQA).