AI Deployment & SRE
vLLM Production Serving: PagedAttention, Continuous Batching & Tensor Parallelism
Deploy open-source LLMs with 10x-24x higher throughput using vLLM's PagedAttention virtual memory architecture and continuous batching.
4 min
How PagedAttention Solves KV Cache Bottlenecks
Traditional LLM serving suffers from memory fragmentation because contiguous KV-cache blocks must be pre-allocated. vLLM uses OS-style virtual memory paging to utilize 96% of GPU VRAM efficiently.
serve.sh
# Production vLLM serving with multi-GPU tensor parallelism
python3 -m vllm.entrypoints.openai.api_server \
--model deepseek-ai/DeepSeek-R1-Distill-Qwen-14B \
--tensor-parallel-size 2 \
--gpu-memory-utilization 0.92 \
--max-model-len 32768 \
--port 8000