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Reddit user RedShiftedTime shared a post about their experience with running large language models (LLMs) locally on a budget setup. They mentioned successfully setting up a local environment using the Club-3090 model, which is now performing at high levels, including processing 4000 prompt tokens per second and executing tool calls at over 110 transactions per second.
This achievement marks significant progress in making LLMs accessible and efficient for users without access to cloud resources. RedShiftedTime noted that their current setup with a Qwen variant running on 262k parameters, 48GB VRAM, is now as powerful as larger models like sonnet but much faster and cost-effective.
- The local model can handle tasks such as making monkey patches and conducting code reviews.
- RedShiftedTime envisions the next upgrade path could involve using a combination of a M5 Ultra with 512GB storage and multiple NVIDIA DGX Sparks for further improvements in processing speed.
- This development is seen as promising, potentially enabling local models to reach frontier-class intelligence within the next year, even if it remains domain-specific.
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### Takeaways
– Local LLMs are now viable alternatives with impressive performance.
– The setup can handle tasks such as code reviews and monkey patches.
– There is potential for these models to achieve significant advancements in processing speed and efficiency.
Originally published at reddit.com. Curated by AI Maestro.
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