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What is currently considered the theoretically optimal quantization bit-width for LLMs? [D]

A Reddit user poses an open-ended technical question about the theoretically optimal quantization bit-width for large language models under fixed memory/compute budgets, citing evolving empirical results and requesting recent research (2025–2026) on scaling laws or large-scale empirical comparisons.

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Reddit r/MachineLearning

Aug 9, 2026

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Research Report on Noise-Shaped One-Bit Coefficients in Discrete Polynomial Fourier Extension

A theoretical mathematics paper introduces noise-shaped one-bit quantization techniques for discrete polynomial Fourier extensions, proving asymptotic approximation rates and establishing identities for error decay under varying smoothness conditions.

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arXiv Computation and Language

Jul 29, 2026

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Break Through the Compression Bottleneck: From Theory to Practice

A new arXiv paper identifies a previously unrecognized non-orthogonality between low-rank decomposition and quantization—two core LLM compression techniques—and introduces Diagonal Adhesive Method (DAM) to mitigate resulting performance degradation.

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arXiv Computation and Language

Jul 24, 2026

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Presentation: Postgres for Production Agents: Your Relational Foundation for Enterprise AI

A presentation advocates PostgreSQL as a relational foundation for production AI agents, highlighting its JSONB parsing, HNSW vector indexing, and vector quantization capabilities to improve LLM context delivery and query speed.

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InfoQ AI / ML / Data Engineering

Jul 15, 2026

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DeepSeek-V4-Flash in MXFP4 is too slow on CPU

A Reddit user reports unexpectedly low inference speed (3.2 tokens/sec) for DeepSeek-V4-Flash quantized in MXFP4 on CPU-only hardware, contrasting with higher expectations based on GLM-5.2 performance and questioning whether MXFP4 is the bottleneck.

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Reddit r/LocalLLaMA

Published Jul 5, 2026 · Analyzed Jul 7, 2026