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Developer creates ultra-lightweight LLM runnable on CPUs

By PulseAugur Editorial · [2 sources] ·

A developer has created a custom quantized large language model, SHADOW-250M-Instruct, with 250 million parameters trained on 30 billion tokens. This model is designed for extreme efficiency, deploying at just 60 MB and requiring minimal RAM, making it runnable on standard laptop CPUs without a GPU. It features a unique long-context mechanism that compresses older information to disk and a novel vocabulary system, enabling it to access up to 100 million tokens of history, though it is primarily trained for retrieval rather than deep reasoning over extended contexts. AI

IMPACT Enables running capable LLMs on low-resource devices, potentially democratizing access and use cases for AI.

RANK_REASON The item describes the creation and technical details of a novel, highly efficient LLM, including its training process, architecture, and performance metrics, which aligns with research and development in the field.

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Developer creates ultra-lightweight LLM runnable on CPUs

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0 / 100
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Research
The item describes the creation and technical details of a novel, highly efficient LLM, including its training process, architecture, and performance metrics, which aligns with research and develop…
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2 independent sources
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model release, infra
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High
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47 days old
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COVERAGE [2]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Final-Data-1410 ·

    I developed my own quantized LLM from scratch, trained on 30B tokens, deploys in 60 MB

    <!-- SC_OFF --><div class="md"><p>Reposting here after sharing this on [<a href="/r/MachineLearning">r/MachineLearning</a>](<a href="/r/MachineLearning">r/MachineLearning</a>) a few days ago, where it got a much better response than I expected (300+ upvotes, great questions, zero…

  2. r/MachineLearning TIER_1 English(EN) · /u/Final-Data-1410 ·

    I developed my own quantized LLM from scratch, trained on 30B tokens, deploys in 60 MB [R]

    <!-- SC_OFF --><div class="md"><p>I trained a 250M parameter model from scratch on 30B tokens of fineweb. It’s quantized to under 2 bits so the whole deployment is 60 MB and it needs about 80 MB of RAM to run. Runs around 400 tok/s on a normal laptop CPU, no GPU needed.</p> <p>Ho…

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