Unsloth releases GLM-5.2 GGUF quantizations
Unsloth has released optimized Dynamic GGUF quantizations for the 744B GLM-5.2 model, shrinking its footprint from 1.51TB to 238GB at 2-bit. This release enables developers to run a frontier-class open-weights model locally on high-end consumer hardware like a 256GB Mac Studio while retaining 82% of the original model's accuracy.
Running a 744-billion parameter model locally on a single machine is a massive milestone that shifts control back to developers.
* The 2-bit quantization dramatically lowers the barrier to entry, shrinking the footprint from 1.51TB to 238GB.
* Retaining 82% of the original accuracy at 2-bit demonstrates the viability of extreme quantization for frontier-class models.
* This empowers developers with strict data privacy requirements to leverage state-of-the-art coding and agentic capabilities offline.
DISCOVERED
1h ago
2026-06-19
PUBLISHED
1h ago
2026-06-19
RELEVANCE
AUTHOR
Better Stack