Evaluating vLLM's Design Choices With Ablation Experiments | HackerNoon
Briefly

from Hackernoon1 year ago
The dynamic block mapping in PagedAttention impacts GPU performance due to accessing the block table and variable sequence lengths, resulting in 20-26% higher latency for attention operations compared to FasterTransformer.
Hackernoonhttps://hackernoon.com/evaluating-vllms-design-choices-with-ablation-experiments
Our experiments show that with careful tuning of block size, vLLM can achieve improved performance by balancing parallelism and fragmentation, demonstrating the significance of architecture design choices in optimizing large language models.
Hackernoonhttps://hackernoon.com/evaluating-vllms-design-choices-with-ablation-experiments
Read at Hackernoon
#llm-optimization #pagedattention #performance-evaluation #gpu-operations #memory-management
[
|
]