In the exploration of LLMs, we observed that Group Query Attention (GQA) and Mixture of Experts (MoE) techniques can significantly enhance both efficiency and performance during inference.
Optimization strategies for LLM inference leverage novel architectural components, which can lead to reductions in latency and increased throughput, addressing common computational bottlenecks.
With GQA specifically, we can focus attention on subsets of the input sequence, resulting in improved resource utilization and faster response times.
The integration of Mixture of Experts in model architectures allows for more adaptable computation during inference, leading to lower resource consumption without sacrificing performance.
#llm-inference #model-architecture-optimization #group-query-attention #mixture-of-experts #hardware-acceleration
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