How to Deploy an LLM with vLLM on Harch Corp GPU Cloud
Step-by-step guide to deploying LLaMA, Mistral, or any HuggingFace LLM with vLLM on Harch Corp GPU cloud.
Create a Harch Corp GPU instance
Sign up at harchcorp.com, create an H100 or A100 instance with your preferred OS (Ubuntu 22.04 recommended). Choose spot for 60% discount if you can handle interruptions.
Install vLLM and dependencies
SSH into your instance. Install vLLM: `pip install vllm`. Install CUDA toolkit if not pre-installed. Verify GPU: `nvidia-smi` should show your H100/A100.
Download your model
Choose a model from HuggingFace (e.g., meta-llama/Llama-2-7b-chat-hf). Set HF_TOKEN environment variable if using gated models. vLLM will download automatically on first run.
Start the vLLM server
Run: `python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1`. For multi-GPU, set tensor-parallel-size to the number of GPUs.
Test the API
Send a test request: `curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{"model": "meta-llama/Llama-2-7b-chat-hf", "prompt": "Hello, world!", "max_tokens": 50}'`. You should get a JSON response with generated text.
Optimize for production
Enable continuous batching (default), set max_num_seqs for batch size, use AWQ or GPTQ quantized models for 2x throughput. Monitor with vLLM's built-in Prometheus metrics.
Set up auto-scaling
Deploy vLLM in Kubernetes with Harch Corp's managed K8s. Use Horizontal Pod Autoscaler to scale based on GPU utilization or request queue. Set up load balancer for traffic distribution.
Monitor and maintain
Use Prometheus + Grafana for monitoring (throughput, latency, GPU utilization). Set up alerts for high latency or low throughput. Update models with rolling deployments (zero downtime).