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I recently came across a post on Reddit about the successful setup of a local LLaMA (Large Language Model) environment. The poster, RedShiftedTime, shared their experience with setting up what they termed as a “2×3090” system—a reference to using two NVIDIA GPUs of 3090 architecture for running large language models locally.
RedShiftedTime reported significant improvements in performance and efficiency. They noted that even a modest setup like theirs, which initially used Windows Subsystem for Linux (WSL2), could achieve impressive results with tools like Club-3090. After switching to Ubuntu as a dual-boot option on the same machine, they observed a substantial increase in performance metrics—4000 prompt processing per second (pp/s) and 113 tool calls per second (tk/s)—all without using NVIDIA’s NVLink for improved GPU communication.
- This setup allows users to run state-of-the-art large language models locally, bypassing the need for expensive cloud infrastructure or proprietary hardware solutions.
- The success of this approach suggests that more accessible and cost-effective local AI environments are becoming feasible, potentially democratizing access to advanced AI capabilities.
- As these setups continue to evolve, they could lead to breakthroughs in smaller model sizes achieving frontier-class intelligence within the next year, indicating a promising trajectory for both research and practical applications of AI.
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Originally published at reddit.com. Curated by AI Maestro.
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