Expired50% confidenceOpinionExact time
Prismo claims its solution activates all 27 billion parameters simultaneously and compresses the original 54 GB QW3.6 model to less than 4 GB, a compression ratio of over 13x, with claimed no impact on performance.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/22/2026
First Seen
Valid until: 10/20/2026(expired)
Sources
Related Claims
UnverifiedPrismML compressed Qwen 3.6 from the original approximately 54 GB to under 4 GB, a compression ratio exceeding 13×.81% similarUnverifiedThe original QW3.6 27B model file size is approximately 54 GB; Prismo compresses it to under 4 GB through mathematical compression techniques, a compression ratio of more than 13×, and claims this does not affect model performance.78% similarUnverifiedAfter quantization, the VRAM requirement of a 7-billion-parameter model is compressed from approximately 28 GB to approximately 4–6 GB, allowing consumer-grade GPUs such as the RTX 3060/4060 to run 7-billion-parameter-class models like Llama 3, Qwen2.5, and Mistral smoothly.73% similarUnverifiedQ3_K_M compresses each weight to approximately 3 bits, reducing the weight size of a 27B model to approximately 12–13 GB.72% similarPartially VerifiedThe QLoRA paper verified that NF4 quantization combined with LoRA fine-tuning can reduce memory requirements from 780GB to 48GB on a 65B parameter model while maintaining performance close to full-precision fine-tuning71% similar
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