From selection to production, 5 tools to help you deploy vLLM, GPUStack, and Qwen. Everything runs locally in your browser—no signup required.
Enter model parameters and precision to quickly estimate if your GPU VRAM is sufficient and avoid OOM errors during deployment.
PrepCompare monthly costs between self-hosted GPU servers and cloud API calls to calculate your payback period.
StartGenerate copy-paste ready vLLM startup commands based on your model, GPU, context length, and quantization settings.
A step-by-step guide to installing vLLM, downloading models, configuring parameters, launching services, and managing multiple models with GPUStack.
Based on 6 months of real-world data, we compare per-1K-token costs, response latency, and privacy controls to help you choose.
A deep dive into diagnosing and fixing vLLM context overflow errors when deploying Qwen, with a three-step recovery plan.
The minimum requirement is an 8GB VRAM GPU (e.g., RTX 3060 Ti), which can run 7B parameter quantized models (AWQ/GPTQ 4-bit). For smooth 13B model operation or running multiple models simultaneously, 24GB VRAM (RTX 3090/4090) is recommended. My current setup uses dual RTX 4090s, which can run Qwen3-7B and Qwen3-Coder-7B in parallel.
vLLM is a production-grade inference engine with high concurrency, continuous batching, and PagedAttention—ideal for API service scenarios. Ollama prioritizes developer experience with simple installation and intuitive commands, perfect for personal experimentation. In short: choose vLLM for services, Ollama for quick trials. For a detailed comparison, see this in-depth comparison.
Five ways to reduce VRAM usage: ① Use AWQ/GPTQ 4-bit quantized models (saves 50-60% VRAM); ② Reduce max-model-len (e.g., from 8192 to 4096); ③ Enable FlashAttention-2 (saves 10-15%); ④ Lower gpu-memory-utilization (e.g., from 0.9 to 0.8); ⑤ Use multi-GPU tensor parallelism (tensor-parallel-size). For detailed steps, see GPU VRAM Optimization Guide.
We recommend Cloudflare Tunnel (free, secure, with HTTPS). No public IP needed, no router changes, no port exposure. I wrote a complete tutorial here. Alternatives include frp, ngrok, and Peanut Shell, but Cloudflare Tunnel wins on stability and security.
It depends on usage. My calculation: a dual RTX 4090 server costs about 35,000 upfront, but at 5,000 requests/day, it saves 2,000-3,000 per month compared to OpenAI API—payback in one year. If you make fewer than 100K requests/month, API is more cost-effective. See the full comparison at Local vs Cloud: Full Comparison.