#large model
7958 related articles
·3 min
Is LLM Inference Actually Profitable? Breaking Down the Cost Structure and Business Logic of Large Model Inference
A deep dive into LLM inference cost structure and profitability models—from GPU throughput, MoE architecture, and KV Cache to scale effects—revealing the business logic behind API price wars.
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·3 min
Should the ACM Digital Library Open Up to Large Language Models? The Battle Between Academic Corpora and AI Training
Exploring whether the ACM Digital Library should open to LLM training. Analyzing the value of academic corpora for AI, data exhaustion concerns, copyright battles, and pragmatic paths including licensing and RAG.
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·3 min
Learning AI Large Language Models: A Complete Roadmap from Beginner to Practitioner
A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.
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·3 min
Apple Silicon LLM Inference Speed Benchmarks: A Complete Guide to Local Large Model Performance on Mac
In-depth analysis of Apple Silicon local LLM inference speed benchmarks covering M-series memory bandwidth, model quantization, MLX framework optimization, and Mac configuration guidance.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy to Help You Fully Understand
Large models aren't search engines — they're more like super compressors. This article explains how LLMs compress data to learn semantic patterns, and explores the phenomenon of intelligent emergence.
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·4 min
What Is the True Nature of Large Language Models? A Super Compressor Analogy That Makes Everything Click
LLMs aren't search engines — they're more like super compressors. This article explains how large models compress corpora to learn semantic patterns, and explores the principles and limitations of emergent intelligence.
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Introduction to How AI Large Models Work: A Must-Read Guide for Test Engineers
A clear explanation of how AI large models work: from concept hierarchy and Transformer mechanics to probabilistic traits, helping test engineers grasp AI testing.
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Introduction to AI Large Language Models: A Must-Read Guide for Test Engineers
A thorough explanation of the essence of AI large language models: from conceptual hierarchy and Transformer mechanics to probabilistic nature, helping test engineers understand LLM strengths and weaknesses.
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From Word Vectors to Large Language Models: The Complete Evolution of AI 2.0 Technology
From word vectors and embeddings to RNNs, BERT, Transformers, and ChatGPT — a complete guide to the technical evolution of large language models and the AI 2.0 era.
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·7 min
AI Daily: T-Head Open-Sources SAIL Software Stack at WAIC, Flurry of Large Model Releases
T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.
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·3 min
Is a Large Language Model Just a Function? AI Principles Explained with Middle School Math
LLMs explained through the lens of functions: input is x, output is y, training solves for parameters, inference computes results. Trillion parameters, next-token prediction — no advanced math needed.
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·3 min
What Is a Large Language Model, Really? The Core Principles Explained Through the Lens of Functions
Demystify large language models using middle-school math: LLMs are complex functions, training solves for parameters, and inference predicts next-token probabilities.
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·3 min
Understanding Large Language Models from Scratch: The Complete AI Evolution from Deep Blue to DeepSeek
A beginner-friendly guide clarifying AI, machine learning, deep learning, and LLMs — tracing the evolution from Deep Blue to AlphaGo, ChatGPT, and DeepSeek.
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·2 min
OpenSpiel 2.0 Released: Reinforcement Learning Framework Fully Embraces Large Language Models
OpenSpiel 2.0 by Google DeepMind adds LLM fine-tuning examples, MCP tool server, JSON trajectories, AlphaZero on JAX, 19 new games, and Windows support.
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·4 min
Getting Started with AI Test Development: Core Principles and Underlying Logic of Large Language Models
New to AI test development? This article breaks down the differences between machine learning and traditional programming, the origins of AI hallucinations, and the core principles of NLP/NLU/NLG to help test engineers build a solid AI knowledge framework.
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GODMODE Project Deep Dive: AI Jailbrea…
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GODMODE Project Deep Dive: AI Jailbreaking Techniques and the Security Arms Race in Large Language Models
GODMODE (G0DM0D3) has 9,300+ GitHub stars fueling debate on AI jailbreaking vs. safety alignment. A deep technical dive into LLM guardrails, prompt injection, and AI security governance.
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Block Low-Rank Compression: A Guide to…
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Block Low-Rank Compression: A Guide to Accelerating Large Model Inference on Memory-Constrained GPUs
Learn how Block Low-Rank (BLR) decomposition compresses large model memory usage and accelerates GPU inference, including CUDA kernel optimization and combination with quantization and pruning.
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·2 min
GPT-5.6 Launches With a Warning: Compute Capacity Is the Real Test for Large Language Models
Altman warned of possible GPT-5.6 service disruptions at launch, highlighting compute capacity as the true bottleneck for LLMs. Here's what it means for users.
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·2 min
AI Large Model Learning Roadmap: A Complete Five-Stage Guide from Zero to Independent Deployment
A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.
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·3 min
AI Large Language Models for Beginners: Core Concepts and Chinese Model Landscape
New to AI? This guide clarifies AI, machine learning, deep learning, and LLMs, traces milestones from Deep Blue to DeepSeek, and maps out China's LLM landscape.
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