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Open-source recipe trains 2B LLMs on consumer GPUs for under $7K

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an open-source pretraining recipe that significantly reduces the cost of training large language models, making them accessible on consumer GPUs for under $7,000. Their Puro-2B model, trained on RTX 5090 GPUs, achieves performance comparable to larger models like Qwen2-1.5B. The study also introduces a "Puro Cost Scaling Law" which estimates that reaching Qwen2-1.5B performance costs less than $5,090, and examines how data curricula influence downstream performance. AI

IMPACT Lowers the barrier for academic and open-source communities to train large language models on accessible hardware.

RANK_REASON The item describes a new open-source training recipe and a model released in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open-source recipe trains 2B LLMs on consumer GPUs for under $7K

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The item describes a new open-source training recipe and a model released in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Puro-2B: Poor Lab's Qwen2-1.5B Trained on RTX 5090 within $5090

    A cost-efficient open-source pretraining recipe trains 2B-parameter models on consumer GPUs for under $7K, yielding performance near larger baselines while deriving cost scaling laws and studying data curricula.

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