Practical solutions to improve Tabby's performance
For the code-completion latency problem, it can be optimized at both hardware and software levels:
- hardware acceleration: must be added
--gpus allParameter Enable GPU Support (NVIDIA cards require 4GB+ video memory) - concurrent processing: Use
--parallelism 4Parameters take full advantage of multi-core CPUs - Model streamlining: Replace lightweight models such as CodeGen-350M (modifications required)
--model(Parameters) - Configuration adjustments: Reduce
max_output_tokensvalue (default 512) reduces the length of generated content - preheating treatment: Keep the service running after the first startup to avoid reloading models
Tests show that on RTX 3060 cards, the GPU-enabled catch-up latency drops from 3.2 seconds to 0.8 seconds. If GPU resources are not available, it is recommended to limit the number of developers using it at the same time and pass thedocker statsMonitor resource usage.
This answer comes from the articleTabby: a native self-hosted AI programming assistant that integrates into VSCodeThe
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