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Found 19 articles

AI Inference Engines Explained: CNNs vs LLMs (2025 Complete Guide)

2025/12/2By Qing Ke Aiin Technology

Discover how AI inference engines evolved from edge-optimized CNNs to cloud-scale LLMs. Learn the key differences between vLLM, TensorRT-LLM, and traditional frameworks like MNN and TVM in this comprehensive 2025 guide.

AI inference engine LLM inference vLLM TensorRT-LLM CNN Transformer edge AI model optimization PagedAttention continuous batching

Ilya Sutskever: The AI 'Age of Scaling' Has Ended — Dawn of the Research Era

2025/11/26By Ji Qi Zhi Xinin Large Language Models

OpenAI co-founder Ilya Sutskever declares the 'Age of Scaling' is over in exclusive interview. Discover why pre-training limits are here, what's next for AI research, and SSI's mission for safe superintelligence.

LLM AI Research AI Safety Superintelligence OpenAI SSI

TensorRT-LLM Tutorial: Deploy LLMs 3x Faster (2025 Setup Guide)

2025/10/15By Qing Ke Aiin Infrastructure

Step-by-step TensorRT-LLM tutorial: Deploy Llama 3/GPT models 3x faster on A100/H100. Includes Python setup, Docker configuration, KV Cache optimization, and benchmarks vs vLLM. Complete guide in 20 minutes.

TensorRT-LLM PyTorch LLM Inference GPU Optimization Infrastructure NVIDIA GB200 MLPerf

Document Chunking for RAG: 9 Strategies Tested (70% Accuracy Boost 2025)

2025/10/11By Ai Yun Shuin Technology

Boost RAG accuracy by 70%: We tested 9 chunking strategies (fixed-size, recursive, semantic). Semantic chunking won. Optimal: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.

document chunking RAG chunking chunking strategies text chunking RAG Retrieval-Augmented Generation

Alibaba's Qwen3-Next: A Deep Dive into its MoE Arch

2025/9/11By Zheng Liin Technology

Explore the architecture of Alibaba's Qwen3-Next, a powerful large language model. Learn about its Mixture of Experts (MoE) design and performance.

Qwen3-Next Alibaba Qwen3-Next large language model Mixture of Experts (MoE)

Build a Llama-Style MoE Model From Scratch (Part 2)

2025/9/9By Zheng Liin Technology

Learn how to train a language model with this PyTorch training loop guide. Explore text generation, the AdamW optimizer, and Mixture of Experts models.

train language model pytorch training loop text generation AdamW optimizer

Build a Llama-Style MoE Model From Scratch (Part 1)

2025/9/8By Zheng Liin Technology

Learn how to build a Llama-style MoE language model from scratch. This guide covers the Mixture of Experts architecture, tokenization, and model setup.

MoE language model Llama-style MoE

Supervised Fine-Tuning: A Guide to LLM Reasoning

2025/9/7By Ning Si Aiin Technology

Learn the complete Supervised Fine-Tuning (SFT) pipeline to enhance LLM reasoning. This guide covers the DeepSeek R1 process, from SFT to knowledge distillation.

Supervised Fine-Tuning SFT pipeline language model fine-tuning DeepSeek R1

Replicate DeepSeek R1 with RL: A Guide

2025/9/3By Ning Si Aiin Technology

Learn to replicate the DeepSeek R1 training process. This guide covers building a reinforcement learning pipeline from scratch using GRPO for advanced LLM reasoning.

DeepSeek R1 Reinforcement Learning Group Relative Policy Optimization GRPO

No-Code LLaMA 3 Fine-Tuning: 3 Steps with LLaMA Factory (2025)

2025/8/6By Number in the Moutainin Technology

Fine-tune LLaMA 3 with zero coding in 3 steps using LLaMA Factory WebUI. Save 80% GPU memory with QLoRA on RTX 3090/4090. Beginner-friendly tutorial with CUDA setup. Supports 100+ models.

LLaMA Factory LLM fine-tuning fine-tune LLM CUDA installation

Transformer Models Explained: Architecture & Attention Guide (2025)

2025/8/4By Quan Ge Tan Aiin Technology

Complete guide to Transformer architecture: self-attention mechanisms, encoder-decoder design, and how Transformers power GPT, BERT, and modern LLMs. With code examples and visual diagrams.

Transformer model Transformer architecture Transformers AI attention mechanism

What Are LLMs? Complete Guide to Large Language Models (2025)

2025/8/1By Quan Ge Tan Aiin Technology

Comprehensive guide to Large Language Models: how LLMs work, Transformer architecture, training process, prompt engineering, and real-world applications. Learn about GPT, Claude, Gemini, and more.

Large Language Models (LLMs)Generative AI Transformer architecture prompt engineering

LLM Inference on H800: A Disaggregated Architecture Guide

2025/7/29By yiakwyin Technology

Explore LLM inference optimization on H800 SuperPods. Learn how a disaggregated architecture with SGLang tackles the prefill bottleneck to boost throughput.

LLM inference disaggregated architecture H800 SuperPod SGLang GPU

GPU Performance: Compute vs Memory-Bound (90% vs 20% Utilization - 2025)

2025/7/25By xiaodong gongin Technology

Master GPU performance optimization: Matrix multiplication achieves 90%+ FLOPS on A100, while CNNs get only 20% due to memory bandwidth bottleneck. Learn compute-bound vs memory-bound operations, fused kernels, Tensor Cores, and H100 FP8 improvements.

Technology AI Innovation GPU GPU optimization Memory bandwidth A100 H100

LLM Architecture Explained: DeepSeek V3 vs Llama 4 (MLA vs GQA 2025)

2025/7/22By Alexin Technology

Compare DeepSeek V3 vs Llama 4 architecture: MLA vs GQA attention, MoE vs dense models. Learn how 671B parameters run at 37B speed. Includes code examples and design trade-offs.

LLM architecture DeepSeek V3 Kimi K2 MoE MLA Transformer AI Innovation

SFT Fine-Tuning: Transform Base LLM to Chat Model (3-Stage Guide - 2025)

2025/7/16By Alexin Technology

Master Supervised Fine-Tuning (SFT) transforming base models to chat assistants. Complete 3-stage pipeline: base → instruct → chat model. LoRA reduces cost 70%, 7B model SFT in 2-4 hours on A100 ($10-20). Alpaca vs Dolly vs Open-Orca datasets compared.

Supervised Fine-Tuning LLM fine-tuning instruction-tuned model chat model SFT LoRA

How Linear Layers Power Multi-Head Attention in Transformers

2025/7/15By Alexin Technology

Discover how linear layers enable multi-head attention in Transformers, powering advanced NLP models with parallel processing and rich representations.

multi-head attention linear layers Transformer architecture query key value

Train 671B DeepSeek V3: RLHF Guide (10x Faster with GRPO - 2025)

2025/7/9By Alexin Technology

Master training 671B parameter LLMs with RL. Solve 5 critical challenges: Megatron vs FSDP, memory offloading, weight conversion, 1000+ GPU scaling. Real DeepSeek V3 workflow with GRPO achieving 10x speedup.

671B parameter LLM Reinforcement Learning RLHF DeepSeek V3 Megatron GRPO large language model training

Qwen3 QK-Norm: Solve FP16 Overflow on Mobile/Edge AI (90% Fewer Errors)

2025/6/26By Alexin Technology

Fix Qwen3 FP16 overflow on mobile devices: QK-Norm explained with code examples. Deploy LLMs on edge hardware (RTX 3060, mobile chips) with 90% error reduction.

Qwen3 QK-Norm attention mechanism FP16 overflow edge AI on-device AI

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