#LLM sparsity
25 related articles
Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Deep Dives·1 min
Qwen3.5 Deep Dive: Hybrid Attention Architecture Achieves 19x Speedup for Long Context
Deep analysis of Alibaba's open-source Qwen3.5 hybrid attention architecture, how Gated Delta Net achieves 19x speedup at 256K context, and multimodal results surpassing Gemini 3 Pro and GPT-5.2.
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Tutorials·4 min
NVIDIA Model Optimizer Post-Training Quantization (PTQ) Practical Guide
A deep dive into NVIDIA Model Optimizer's PTQ workflow, covering INT8/INT4 quantization principles, calibration methods, RTX GPU optimization, and best practices for deploying quantized LLMs on consumer GPUs.
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Deep Dives·3 min
Microsoft Tutel: A Deep Dive into the MoE Model Acceleration Library with FP4/FP8 Low-Precision Support
A deep dive into Microsoft's open-source Tutel MoE optimization library, supporting FP8, NVFP4, and MXFP4 multi-precision computation for DeepSeek, Kimi-K2, Qwen3, and other leading MoE models.
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Tech Frontiers·2 min
DeepSeek V3.2 Released: Proprietary Sparse Attention (DSA) + 50% API Price Cut Explained
DeepSeek releases V3.2-Exp with proprietary DeepSeek Sparse Attention (DSA) for faster long-context training and inference, plus API prices cut over 50%.
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