The Rise of vLLM in Modern Cloud Development: Revolutionizing AI Inference
Large Language Models (LLMs) are powerful but expensive to run in the cloud because they require large amounts of GPU memory. During inference, they generate text token-by-token and store intermediate results in a growing Key-Value (KV) cache. Traditional systems allocate this cache in large, contiguous memory blocks, which causes severe GPU memory fragmentation and waste—up to 80% of memory remains unused, limiting concurrency and throughput.
