A user on r/LocalLLaMA shared surprising results regarding the performance of running large language models with a dual-GPU setup where one GPU was connected via a PCIe 3.0 x1 slot. Contrary to expectations that this limited bandwidth would cause significant bottlenecks, the user found that tensor splitting, a method for distributing model layers across GPUs, yielded a substantial performance increase of over 40% compared to layer splitting. Further testing on a rented machine with a different model also showed significant speedups with tensor splitting, with PCIe bandwidth measurements indicating that the x1 connection was not a major bottleneck during inference, though it did impact model loading times. AI
IMPACT Suggests that lower-bandwidth PCIe configurations may be viable for certain LLM inference tasks, potentially reducing hardware upgrade costs for users.
RANK_REASON User-generated content sharing personal findings and results, not a primary source release or official announcement.
- Intel i5-14400F
- L3.1 Nemotron Sunfall 71B IQ3_XS
- llama.cpp
- RTX 5060 Ti 16GB
- PCIe 3.0 x1
- RTX 3060 12GB
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