An unofficial companion guide for developers following vllm-project/vllm.
This repo is independently written. It is not affiliated with the upstream project, DeepSeek, OpenAI, or any model provider. It does not copy upstream code, prompt collections, images, model weights, private docs, or branding assets.
- Project: vllm-project/vllm
- Official reference used here: https://huggingface.co/collections/deepseek-ai/deepseek-v4
vLLM is one of the first places developers check when new open-weight models arrive, especially for high-throughput serving and long-context deployment.
A serving-oriented companion covering what to verify before advertising DeepSeek V4 support: model card compatibility, tensor parallel settings, context limits, memory estimates, and throughput notes.
- Verify model architecture support in the installed vLLM version.
- Confirm tokenizer and chat template behavior.
- Benchmark short, medium, and long-context requests.
- Publish exact GPU, driver, and command-line settings.
- Keep API keys in environment variables or a secret manager.
- Preserve upstream attribution when sharing screenshots, prompts, adapters, or benchmark results.
- Do not present this repo as an official upstream release.
- Check each upstream project's license before copying code or assets.
- If you publish examples, include model name, date, parameters, and provider.
DeepSeek V4 open weights make serving questions immediate. I made a vLLM companion checklist for anyone testing deployment and throughput.
- Upstream project: https://github.com/vllm-project/vllm
- Official reference: https://huggingface.co/collections/deepseek-ai/deepseek-v4