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AI2ML AI to Machine Learning
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AI2ML AI to Machine Learning

Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi

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Latest from AI2ML AI to Machine Learning

53 recent articles
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 27, 2025 · Artificial Intelligence

Why Jeff Dean Champions Speculative Decoding: The Underlying Ideas

Jeff Dean highlighted speculative decoding as a lossless inference acceleration technique that can boost large language model throughput by 2–3×, and the article breaks down its core concepts—including parallel token verification, draft‑target model collaboration, rejection sampling theory, and practical optimizations such as continuous batching and tree‑based verification.

Draft-Target ModelInference AccelerationKV Cache
0 likes · 8 min read
Why Jeff Dean Champions Speculative Decoding: The Underlying Ideas
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 22, 2025 · Artificial Intelligence

The Core Ideas Behind Paged Attention for KV‑Caching

This article explains how Paged Attention, introduced by the vLLM team, applies virtual‑memory techniques, non‑contiguous block mapping, copy‑on‑write reuse, distributed scheduling, and hardware‑level optimizations to improve KV‑cache efficiency and reduce memory fragmentation in large language model serving.

Copy-on-WriteGPU Memory ManagementKV-Caching
0 likes · 6 min read
The Core Ideas Behind Paged Attention for KV‑Caching
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 21, 2025 · Artificial Intelligence

Why KV Caching Is Critical for Efficient LLM Inference

The article breaks down the principles of KV caching in large language models, explaining how Q/K/V behavior differs between training and inference, the role of prompts, cache size trade‑offs, and the complexities of concurrent inference, all backed by concrete examples and references.

Concurrent InferenceKV CacheLLM Inference
0 likes · 7 min read
Why KV Caching Is Critical for Efficient LLM Inference
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 19, 2025 · Artificial Intelligence

The 9 Key Ideas Behind FlashAttention

FlashAttention accelerates transformer inference by combining nine techniques—including loss‑less attention, GPU memory‑pyramid optimization, SRAM‑reusing tiling, safe softmax scaling, online buffering, tile‑size constraints, parallel multiplication, reduced KV slicing, and integrated backward‑pass caching—to achieve efficient, high‑throughput computation on modern GPUs.

Attention MechanismFlashAttentionGPU optimization
0 likes · 8 min read
The 9 Key Ideas Behind FlashAttention
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 16, 2025 · Industry Insights

Why Computer Science Majors Must Embrace a Massive Paradigm Shift

The article argues that traditional storage‑centric computer science curricula are becoming obsolete as AI‑driven, compute‑centric paradigms dominate hardware, data‑center operations, and software ecosystems, urging universities and students to rapidly adopt new teaching focus and skills.

AI hardwareCUDAassociative memory
0 likes · 10 min read
Why Computer Science Majors Must Embrace a Massive Paradigm Shift
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 3, 2025 · Artificial Intelligence

2026 Forecast: How Large‑Model AI Will Evolve After 2025 Breakthroughs

The article reviews the major 2025 breakthroughs in multimodal, open‑source, and deployment technologies for large models and outlines four 2026 trends—including ToC vs. ToB service split, dual‑hand data generation, MoE routing advances, and AI4Science breakthroughs—that will shape the next wave of AI development.

AI DeploymentAI4ScienceLarge Language Models
0 likes · 6 min read
2026 Forecast: How Large‑Model AI Will Evolve After 2025 Breakthroughs
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Nov 4, 2025 · Artificial Intelligence

Common Debugging Signals for Large Language Models

This article outlines the end‑to‑end workflow for large‑model training, highlights typical debugging challenges such as memory OOM, performance bottlenecks, and gradient issues, and provides concrete strategies, tools (DeepSpeed, Megatron, Torchtitan, veScale) and best‑practice checklists to help engineers diagnose and resolve problems efficiently.

DeepSpeedLLMMegatron
0 likes · 12 min read
Common Debugging Signals for Large Language Models
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Nov 3, 2025 · Artificial Intelligence

Smol Training Playbook: Secrets to Building World-Class LLMs

The article details the SmolLM3 3B‑parameter model, its architecture, dual‑mode inference, a three‑stage data‑curation strategy, rigorous ablation methods, preference optimisation (APO/DPO), model merging, and practical training‑stability tricks, offering a comprehensive guide for building high‑performing large language models.

APOLLM-trainingModel Merging
0 likes · 13 min read
Smol Training Playbook: Secrets to Building World-Class LLMs
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 24, 2025 · Industry Insights

How Generative AI Is Fueling a New Wave of Insurance Fraud

Generative AI tools like DALL·E, Midjourney and deep‑fake platforms are enabling criminals to create highly realistic images, videos and documents, leading to a surge in sophisticated insurance fraud across auto, property, health and life lines, and forcing insurers to overhaul detection and regulatory practices.

AI detectiondeepfakegenerative AI
0 likes · 13 min read
How Generative AI Is Fueling a New Wave of Insurance Fraud