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27B LLM compressed to 8.31 GB, runs on 12GB laptop GPU

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to significantly reduce the size of large language models, enabling a 27-billion parameter model, Qwen3.8-27B, to fit onto a 12 GB laptop GPU. This was achieved by compressing the model's weights to approximately 2.5 bits per weight, resulting in an 84.5% size reduction from its original 53.8 GB. The new compressed model, named Pingala-8, also demonstrated improved performance on the WikiText-2 perplexity benchmark, although a competing format, DASH-Q IQ2_XS, still shows superior results. The compressed model requires a patched version of the llama.cpp runtime for NVIDIA GPUs and is not compatible with standard tools like Ollama or LM Studio. AI

IMPACT Enables running larger models on consumer hardware, potentially democratizing access to advanced AI capabilities.

RANK_REASON The item details a novel compression technique for LLMs and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

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

27B LLM compressed to 8.31 GB, runs on 12GB laptop GPU

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The item details a novel compression technique for LLMs and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Genrius AI ·

    How we fit a 27B model on a 12 GB laptop GPU

    <h2> In short </h2> <ul> <li>Qwen3.8-27B goes from 53.8 GB of 16-bit weights to an 8.31 GB Pingala-8 file, 84.5% smaller (6.5x). It runs with every layer on one 12 GB RTX 5070 Ti Laptop GPU at 37.7 tokens/s (llama-bench, batch 1). </li> <li>Version 2 moves bits between tensor rol…

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