A Reddit user detailed their optimized setup for running large language models on a dual RTX 5060 Ti GPU configuration. Key recommendations include ensuring the motherboard utilizes CPU lanes for both GPUs via bifurcation, dedicating a separate GPU for system display to preserve VRAM, and using NVFP4 quantization for better performance. The user achieved impressive prefill speeds of up to 4,270 tokens/s and decode speeds of 70-95 tokens/s with a 176K context window, attributing much of this to hardware configuration and specific model optimizations like MTP speculative decoding and int8 KV cache. AI
IMPACT Provides insights into efficient hardware configurations for running large language models locally.
RANK_REASON User-generated guide on optimizing hardware for LLM inference.
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