Reducing latency requires multi-step optimization:
- model level: Select a lightweight model such as gpt-oss-20b and
llama-serverAdd at startup-fa(flash attention) parameter accelerated reasoning. - Hardware configuration: Ensure that the GPU driver is up-to-date and CUDA core acceleration is enabled; if using a CPU, a processor with at least 8 threads is recommended.
- Pipeline Optimization: Adjust the buffer size of the Pipecat framework to reduce the voice transmission queue wait time.
- real time prioritization: Set Python processes to high priority in the operating system to avoid resource contention.
Developers can also use logs to analyze the time consumption of each module and optimize bottlenecks.
This answer comes from the articlegpt-oss-space-game: a local voice-interactive space game built using open-source AI modelsThe
May not be reproduced without permission:AI productivity tools " How to optimize the voice interaction latency problem in gpt-oss-space-game?
