New free learning path on the Red Hat Developer Sandbox: compress, serve, and benchmark a model with @vllm_project, hands-on in Jupyter, no GPU needed. Here's what you'll actually do: ⚡ Quantize Qwen3 to W4A16 with LLM Compressor using GPTQ. Measure the result: 42% smaller, 8.2% perplexity increase. Learn to decide if that tradeoff fits your use case. 🚀 Connect to a running vLLM server and send requests via the OpenAI-compatible API. Watch 5 concurrent requests handled in real time. See prefix cache queries increment live via the Prometheus metrics endpoint. 📊 Run a GuideLLM benchmark: TTFT, inter-token latency, and E2E latency at p50, p95, and p99. Run Hellaswag with lm_eval. Cross-reference with the published model card to make a deployment decision backed by numbers. Less than an hour to complete. Free account. Built by @cedricclyburn and Michael Santos. 🙏