The article presents an updated practical guide for deploying EAGLE-3 speculative decoding with vLLM using the verifier Qwen/Qwen3-30B-A3B-Instruct-2507 and the matching draft model RedHatAI/Qwen3-30B-A3B-Instruct-2507-speculator.eagle3, replacing an outdated Colab notebook that used Qwen3-32B. The approach requires Python 3.10+, transformers==5.12.1, requests, and a current vLLM build with EAGLE-3 support. First, the tokenizer and chat template are validated via AutoTokenizer.apply_chat_template, confirming Qwen chat markers render correctly. Then vLLM is launched with the long-form command specifying the verifier, tensor parallelism, and a speculative-config JSON setting method to eagle3, num_speculative_tokens to the published default of 3, and the draft model identifier. A shorter model-embedded serve path exists but the explicit form clarifies the verifier-speculator coupling. Requests are sent to the OpenAI-compatible /v1/chat/completions endpoint with low temperature (0.2) and max_tokens 192. The article emphasizes that EAGLE-3 drafts are model-specific and cannot be mixed across verifiers. Benefits appear for predictable workloads like code generation and structured output at low concurrency; high concurrency or creative writing often erases gains. Practitioners should compare speculative and non-speculative runs on identical prompts, measuring tokens per second, time to first token, and time per output token together, as acceptance rates and latency profiles vary.