Self-Hosting DeepSeek-R1 and Qwen 2.5 on Private GPUs with vLLM and PagedAttention

The release of DeepSeek-R1 and Qwen 2.5 Coder has completely shifted the economics of AI. Open-weight reasoning models now match or exceed proprietary models on coding and mathematical benchmarks, giving engineering teams the power to run frontier-tier AI entirely behind their corporate firewall.

In this technical walkthrough, I detail how to deploy DeepSeek-R1 Distill and Qwen 2.5 models on private GPU nodes (NVIDIA A10G / L40S) using vLLM to maximize token generation throughput via PagedAttention.

Deploying vLLM with Docker and OpenAI-Compatible API

docker run --gpus all --shm-size 16g -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface vllm/vllm-openai:latest --model deepseek-ai/DeepSeek-R1-Distill-Qwen-14B --max-model-len 8192 --gpu-memory-utilization 0.95 --enforce-eager