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.
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.
# 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 8000Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
TensorRT-LLM & NVIDIA Triton: Enterprise Inference Optimization & FP8 Quantization
Achieve maximum GPU utilization and lowest latency on NVIDIA H100/A100 clusters with TensorRT-LLM and Triton Inference Server.
Speculative Decoding: 2x-3x Faster Inference with Zero Loss in Output Quality
Accelerate token generation using a lightweight draft model paired with a large model verifier without sacrificing perplexity or accuracy.
Generative Engine Optimization (GEO) Guide: Complete AI Search Readiness Checklist & Best Practices
A comprehensive guide to ranking, getting cited, and becoming a primary authoritative source on ChatGPT, Perplexity, and Google AI Overviews through modern GEO architecture and schemas.