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quantization-aware training

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21CTO
21CTO
Jul 17, 2026 · Artificial Intelligence

Kimi K3 Unveiled: 2.8 Trillion‑Parameter Open‑Source LLM Sets New Record

On July 16, the Moon‑of‑Darkness team released Kimi K3, a 2.8‑trillion‑parameter open‑source large language model that introduces mixed‑linear attention, attention residuals, and a highly efficient Mixture‑of‑Experts design, achieving roughly 2.5× the scaling efficiency of its predecessor while approaching the performance of top closed‑source models.

Kimi K3Mixture of ExpertsOpen source AI
0 likes · 6 min read
Kimi K3 Unveiled: 2.8 Trillion‑Parameter Open‑Source LLM Sets New Record
Architect
Architect
Mar 5, 2025 · Artificial Intelligence

How Does Quantization Shrink LLMs? A Deep Dive into GPTQ, GGUF, and Techniques

This article explains why large language models need quantization, describes the core concepts, classification schemes, symmetric and asymmetric methods, handling of outliers, and compares post‑training quantization (PTQ) with quantization‑aware training (QAT), while detailing popular techniques such as GPTQ, GGUF, and BitNet.

AI hardwareGGUFGPTQ
0 likes · 25 min read
How Does Quantization Shrink LLMs? A Deep Dive into GPTQ, GGUF, and Techniques
Baidu Geek Talk
Baidu Geek Talk
Jun 26, 2023 · Artificial Intelligence

INT8 Quantization for Baidu Search Semantic Models (ERNIE)

Baidu applied large‑scale INT8 quantization to its ERNIE search semantic models, achieving over 25% inference speedup with less than 1% degradation in relevance metrics by selectively quantizing less‑sensitive fully‑connected layers, using automated calibration, hyper‑parameter tuning, and techniques such as QAT and SmoothQuant, while paving the way for even lower‑bit quantization and token pruning.

ERNIEINT8 quantizationSmoothQuant
0 likes · 15 min read
INT8 Quantization for Baidu Search Semantic Models (ERNIE)