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post-training quantization

topic4 events
papersTODAY 04:00 UTC

WaterKron Method Ties Kronecker-Factored Hessian Choice to Information Theory for Quantization

A new preprint introduces WaterKron, a post-training quantization approach that pairs two-sided GPTQ with waterfilling-based scaling that varies by row and column, along with entropy coding. The authors also present FlipFlop Hessian, a way of selecting Kronecker-factored Hessian approximations that they ground in information-theoretic arguments. The work targets how such approximations should be chosen when compressing neural networks after training.

quantization/llm-inference/arxiv/compression
papersSEP 11 04:00 UTC

RDQ quantization method targets accuracy loss below 4-bit in LLMs

A research paper proposes Residual Distribution Quantization, a post-training quantization approach for large language models. The authors attribute the sharp accuracy drop seen below 4-bit precision to distributional drift in the residual stream, where quantization error introduced at each transformer layer builds up in the shared representation. Their method aims to correct this accumulated error to preserve model quality at lower bit widths.

quantization/llm/research/efficiency
papersSEP 11 04:00 UTC

REAL-Q: End-to-End LLM Quantization via Dynamic Gradient Descent

A new arXiv paper presents REAL-Q, a post-training quantization method that adjusts a model's compressed weights end-to-end rather than optimizing each layer with its own solver. It replaces the single closed-form second-order solution used by current PTQ techniques with dynamic gradient descent. The work targets cheaper deployment of large language models under tight memory and compute limits.

quantization/llm-inference/efficiency/arxiv
papersSEP 10 04:00 UTC

Researchers scale post-training ternarisation to Qwen3-8B with 1.58-bit packed execution

An arXiv paper reports scaling an aggressive post-training ternarisation pipeline to the 8B-parameter Qwen3 model, measuring how much capability is retained after conversion to ultra-low-bit weights. The authors argue that a nominal 1.58-bit label alone does not specify the actual deployed representation or its runtime cost, so they additionally present a lossless packing scheme and a method for executing the model directly on packed weights. The work includes reproduction details so that the conversion results can be independently replicated.

quantization/efficient-inference/qwen3/research