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The post discusses significant improvements in running large language models (LLMs) locally, specifically mentioning the successful deployment and performance of a local setup using the club-3090 model. The author notes that this has led to substantial increases in processing power and efficiency compared to previous setups.
- Local LLMs are now viable for use cases previously requiring cloud-based services, such as tool-calling and prompt processing.
- The performance gains have been dramatic, with the author reporting 4000 PP/s and 113 TK/s on a local setup without NVLink, indicating significant improvements in throughput compared to previous setups.
- These advancements suggest that smaller models could potentially achieve frontier class intelligence within the next year, opening up new possibilities for AI development and deployment outside of cloud environments.
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### Takeaways:
– Local LLMs are now more viable and performant compared to previous setups.
– Significant performance improvements in local processing power and efficiency.
– Smaller models might achieve frontier class intelligence within the next year.
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