Your LLM Server Is Wasting 80% of Its GPU Memory — Here’s How vLLM Fixes That
Large language models (LLMs) face a significant bottleneck in serving efficiency due to the memory demands of KV cache, which stores intermediate attention calculations. This KV cache, essential for enabling faster responses and handling longer context windows, can consume up to 80% of GPU memory. Innovations like vLLM's PagedAttention, inspired by operating system memory management, are addressing this by optimizing KV cache storage and reducing memory fragmentation, leading to substantial improvements in inference throughput. AI
IMPACT Optimizing KV cache and memory usage is crucial for reducing LLM serving costs and improving inference speed, enabling wider adoption of AI applications.