The article discusses vAttention, a novel system designed to improve performance and memory efficiency in serving large language models (LLMs). It highlights issues with the traditional PagedAttention model, which suffers from redundancy and performance overhead due to its attention kernel. vAttention introduces optimizations by configuring block sizes based on tensor parallelism (TP) dimensions, thus controlling physical memory allocation and reducing fragmentation. Additionally, low-level CUDA support is leveraged to hide memory allocation latency. The research showcases evaluation results that indicate vAttention's capacity to enhance throughput while minimizing wasted memory.
The vAttention system optimizes physical memory allocation, significantly reducing memory fragmentation and improving serving performance by adjusting block sizes according to TP configurations.
By utilizing low-level CUDA support and optimizing physical memory allocation strategies, vAttention minimizes latency issues associated with memory allocation.
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