1.9 KiB
Frequently Asked Questions
Q: How many chips do I need to infer a model in vLLM-Omni?
A: Now, we support natively disaggregated deployment for different model stages within a model. There is a restriction that one chip can only have one AutoRegressive model stage. This is because the unified KV cache management of vLLM. Stages of other types can coexist within a chip. The restriction will be resolved in later version.
Q: When trying to run examples, I encounter error about backend of librosa or soundfile. How to solve it?
A: If you encounter error about backend of librosa, try to install ffmpeg with command below.
sudo apt update
sudo apt install ffmpeg
Q: I see GPU OOM or "free memory is less than desired GPU memory utilization" errors. How can I fix it?
A: Refer to GPU memory calculation and configuration for guidance on tuning gpu_memory_utilization and related settings.
Q: I encounter some bugs or CI problems, which is urgent. How can I solve it?
A: At first, you can check current issues to find possible solutions. If none of these satisfy your demand and it is urgent, please find these volunteers for help.
Q: Does vLLM-Omni support AWQ or any other quantization?
A: vLLM-Omni partitions model into several stages. For AR stages, it will reuse main logic of LLMEngine in vLLM. So current quantization supported in vLLM should be also supported in vLLM-Omni for them. But systematic verification is ongoing. For quantization for DiffusionEngine, we are working on it. Please stay tuned and welcome contribution!
Q: Does vLLM-Omni support multimodal streaming input and output?
A: Not yet. We already put it on the Roadmap. Please stay tuned!