# GSQ Explained: Smaller Quants, Same Great Performance **URL:** \ **Category:** Content Creators\ **Tags:** onchain-ai-garage, machine-learning, quantisation\ **Created:** [30 September 2026 14:00 UTC](https://www.artofsm.art/t/gsq-explained-smaller-quants-same-great-performance/24658 "2026-09-30T14:00:23Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [30 September 2026 14:00 UTC](https://www.artofsm.art/t/gsq-explained-smaller-quants-same-great-performance/24658/1 "2026-09-30T14:00:23Z")
[![](https://www.artofsm.art/uploads/default/original/3X/0/f/0f524c9bcb2fcc0f33784214988efa5b8f66a303.jpeg "GSQ Explained: Smaller Quants, Same Great Performance") ](https://www.youtube.com/watch?v=9ercn6wWefw) GSQ is an advanced quantization method for large language models that uses probabilistic training with Gumbel Softmax sampling to compress weights into very low bit levels, achieving smaller model sizes without sacrificing accuracy. Combined with Resource-Constrained Optimization for mixed-precision allocation, GSQ outperforms or matches other quantization formats in benchmarks, though it requires extensive training on powerful hardware. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [30 September 2026 14:24 UTC](https://www.artofsm.art/t/gsq-explained-smaller-quants-same-great-performance/24658/3 "2026-09-30T14:24:37Z")
The video begins by introducing GSQ, a novel quantization method for large language models (LLMs) that reduces model size by compressing weights into fewer bits, enabling large models to run on smaller hardware. Unlike traditional quantization methods that snap weights to the nearest allowed value using a fixed “ruler,” GSQ employs a more dynamic approach. It treats quantization as a training process rather than a simple conversion, using a probabilistic method called Gumbel Softmax sampling to explore multiple possible quantized values before settling on the best fit. This approach allows GSQ to achieve high accuracy even at very low bit levels, such as 2 to 3 bits, which is smaller than other popular formats like NVFP4. The GSQ method was developed by a European research team from ISTA in Austria, ETH Zurich, TU Wien, and Red Hat AI, building on previous work like GPTQ and Mask LLMs. The key innovation is that instead of greedily assigning each weight to the closest quantization tick, GSQ assigns probabilities to neighboring ticks and iteratively refines these assignments during a full training run. This process, which cools from a high “temperature” allowing exploration to a low temperature for confident decisions, helps the model find better quantization points and maintain accuracy. Most weights remain close to their greedy assignments, but the few that shift improve overall performance. A complementary technique called RCO (Resource-Constrained Optimization) is often used alongside GSQ to allocate bits efficiently across different model layers. RCO decides how many bits each tensor should get within a fixed budget, prioritizing important weights with higher precision and compressing less critical ones more aggressively. This mixed-precision approach results in a single quantized file containing multiple bit levels, optimizing size and performance. For example, the tested GSQ model averaged about 3.5 bits per weight, significantly smaller than comparable models using other quantization methods. The video then presents benchmark results comparing GSQ to other quantization formats like EXL3 and NVFP4. Despite its smaller size, the GSQ model performed similarly or better on tasks such as math problem-solving and instruction following. While generation speed was comparable, GSQ’s smaller file size did not translate into faster performance, likely due to the complexity of handling multiple bit precisions in one file. Prefill speed was stable for shorter contexts but dropped off at very long contexts, whereas decode speed was similar across formats. In conclusion, GSQ offers a promising approach to quantization by enabling smaller model sizes without sacrificing quality, especially at very low bit levels where other methods struggle. However, it requires a lengthy training process on powerful hardware, making it less accessible for casual users who cannot generate their own GSQ models. Despite this, the availability of pre-quantized GSQ models and its strong performance make it a popular choice in the community. The presenter encourages viewers to share their experiences with GSQ and expresses interest in exploring more models using this method in the future.