inference optimization

JD Retail Technology

Apr 22, 2025 · Artificial Intelligence

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

JD’s advertising platform replaces rule‑based recall with a generative large‑model pipeline that unifies e‑commerce knowledge, multimodal user intent, and semantic IDs across recall, coarse‑ranking, fine‑ranking and creative optimization, while meeting sub‑100 ms latency and sub‑¥1‑per‑million‑token cost through quantization, parallelism, caching, and joint generative‑discriminative inference, delivering double‑digit performance gains and paving the way for domain‑specific foundation models.

Large Modelsadvertisingdistributed systems

0 likes · 20 min read

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

58 Tech

Apr 11, 2025 · Artificial Intelligence

Optimization of Multimodal Visual Large Model Inference: Pre‑processing, ViT TensorRT, CUDA Graphs, Tokenization, Prefix Cache, and Quantization

This report details a comprehensive set of optimizations for multimodal visual large‑model (VLM) inference—including image pre‑processing acceleration, TensorRT integration for the ViT module, CUDA‑Graph replay, token‑count reduction, prefix‑cache handling, and weight quantization—demonstrating up to three‑fold throughput gains while maintaining accuracy.

CUDA GraphTensorRTVisual Language Model

0 likes · 19 min read

Optimization of Multimodal Visual Large Model Inference: Pre‑processing, ViT TensorRT, CUDA Graphs, Tokenization, Prefix Cache, and Quantization

Java Architecture Diary

Mar 7, 2025 · Artificial Intelligence

Boost Inference Efficiency with QwQ-32B: Benchmarks, Resource Savings, and Java Integration

QwQ-32B, Alibaba’s new inference‑optimized large language model built on the Qwen2.5 architecture, outperforms DeepSeek‑R1 across math reasoning, code generation, and safety benchmarks while requiring only 24 GB vRAM, and the article provides detailed performance data, resource‑efficiency analysis, and step‑by‑step Java and Ollama integration instructions.

Java integrationbenchmarkfunction calling

0 likes · 7 min read

Boost Inference Efficiency with QwQ-32B: Benchmarks, Resource Savings, and Java Integration

DeWu Technology

Feb 12, 2025 · Artificial Intelligence

Edge Intelligence for Intelligent Video Cover Recommendation

The article describes an edge‑based video‑cover recommendation system for DeWu that leverages the MNN SDK and a lightweight MobileNetV3 model, performing on‑device inference with quantization and parallel processing to automatically select high‑quality covers, achieving sub‑second latency and boosting click‑through rates by up to 18 %.

Model DeploymentVideo Coveredge AI

0 likes · 12 min read

Edge Intelligence for Intelligent Video Cover Recommendation

JD Retail Technology

Feb 12, 2025 · Artificial Intelligence

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

JD Advertising accelerates its generative‑recall recommendation system by integrating NVIDIA TensorRT‑LLM, which simplifies the pipeline, injects LLM knowledge, scales to billions of parameters, and delivers over five‑fold throughput gains, one‑fifth the cost, and significant CTR improvements in both recommendation and search.

LLMRecommendation systemsTensorRT-LLM

0 likes · 13 min read

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

DataFunTalk

Jan 26, 2025 · Artificial Intelligence

58.com’s LingXi Large Language Model Platform: Development, Deployment, and Performance Optimizations

Since the launch of ChatGPT, 58.com has built a Model‑as‑a‑Service platform called LingXi that trains and serves domain‑specific large language models, supports over a hundred internal scenarios with daily inference exceeding ten million calls, and continuously improves performance through quantization, GPU optimization, model miniaturization, and advanced AI applications such as interview assistants, voice agents, and RAG‑enabled agents.

AI PlatformAI applicationsLLM

0 likes · 9 min read

58.com’s LingXi Large Language Model Platform: Development, Deployment, and Performance Optimizations

JD Tech Talk

Jan 14, 2025 · Artificial Intelligence

Advantages and Engineering Implementation of Generative Recommendation Systems Using Large Language Models

This article explains how generative recommendation systems powered by large language models simplify the recommendation pipeline, integrate world knowledge, benefit from scaling laws, and require specialized engineering optimizations such as TensorRT‑LLM deployment, inference acceleration, and hybrid model strategies to achieve low latency and high throughput in real‑world e‑commerce scenarios.

AILLMTensorRT-LLM

0 likes · 10 min read

Advantages and Engineering Implementation of Generative Recommendation Systems Using Large Language Models

DataFunSummit

Dec 31, 2024 · Artificial Intelligence

How Momo Leverages Large Model Technology to Transform Business and R&D Processes

This article explains how Momo utilizes large language model technologies to revamp its AI application paradigm, achieve efficient inference through quantization and prefix caching, build a workflow‑based model platform, and outline future plans for framework optimization and multimodal support.

AI PlatformLarge Language ModelsMOMO

0 likes · 16 min read

How Momo Leverages Large Model Technology to Transform Business and R&D Processes

DataFunSummit

Dec 4, 2024 · Artificial Intelligence

Accelerating Large Language Model Inference with the YiNian LLM Framework

This article presents the YiNian LLM framework, detailing how KVCache, prefill/decoding separation, continuous batching, PageAttention, and multi‑hardware scheduling are used to speed up large language model inference while managing GPU memory and latency.

AI accelerationGPUKVCache

0 likes · 20 min read

Accelerating Large Language Model Inference with the YiNian LLM Framework

DataFunSummit

Nov 22, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec’s recommendation system architecture, detailing training and inference optimizations, embedding parallelism, CPU/GPU placement strategies, online learning pipelines, and network compression techniques that together improve scalability, latency, and cost efficiency.

EasyRecTraining Optimizationdistributed systems

0 likes · 15 min read

EasyRec Recommendation Algorithm Training and Inference Optimization

DataFunSummit

Nov 4, 2024 · Artificial Intelligence

Performance Optimization Techniques for Large Model Inference Frameworks

This article outlines four key optimization areas for large model inference frameworks—quantization, speculative sampling, TTFT/TPOT improvements, and communication optimization—detailing specific techniques, experimental results, and practical benefits such as reduced memory usage, lower latency, and higher throughput.

AIPerformanceinference optimization

0 likes · 12 min read

Performance Optimization Techniques for Large Model Inference Frameworks

Sohu Tech Products

Aug 28, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

EasyRec, Alibaba Cloud’s modular recommendation framework, unifies configurable data, embedding, dense, and output layers on MaxCompute, EMR, and DLC, and speeds training with deduplication, EmbeddingParallel sharding, lock‑free hash tables, GPU embeddings, and AMX BF16, while inference benefits from operator fusion, low‑precision AVX/AMX kernels, compact caches, batch merging, and network compression, enabling real‑time online learning and delivering higher recommendation quality at lower cost in e‑commerce.

Alibaba CloudEasyRecTraining Optimization

0 likes · 14 min read

DataFunTalk

Aug 26, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec's recommendation system architecture, detailing training and inference optimizations, distributed deployment strategies, operator fusion techniques, online learning pipelines, and network-level improvements to enhance performance and scalability.

AITraining Optimizationdistributed systems

0 likes · 15 min read

Baidu Tech Salon

May 15, 2024 · Artificial Intelligence

Accelerating Large Model Training and Inference with Baidu Baige AIAK‑LLM

Baidu Baige’s AIAK‑LLM suite accelerates large‑model training and inference by boosting Model FLOPS Utilization through techniques such as TP communication overlap, hybrid recompute, zero‑offload, automatic parallel‑strategy search, multi‑chip support, and inference‑specific optimizations, achieving over 60 % speedup and seamless Hugging Face integration.

AI infrastructureAIAK-LLMBaidu Baige

0 likes · 26 min read

Accelerating Large Model Training and Inference with Baidu Baige AIAK‑LLM

Baidu Geek Talk

May 15, 2024 · Artificial Intelligence

Accelerating Large Model Training and Inference with Baidu Baige AIAK‑LLM: Challenges, Techniques, and Optimizations

The talk outlines how Baidu’s Baige AIAK‑LLM suite tackles the exploding compute demands of trillion‑parameter models by boosting Model FLOPS Utilization through advanced parallelism, memory‑saving recompute, zero‑offload, adaptive scheduling, and cross‑chip orchestration, delivering 30‑60% training and inference speedups and a unified cloud product.

AI infrastructureBaiduLarge Language Models

0 likes · 25 min read

Accelerating Large Model Training and Inference with Baidu Baige AIAK‑LLM: Challenges, Techniques, and Optimizations

iQIYI Technical Product Team

Mar 15, 2024 · Artificial Intelligence

Optimizing GPU Inference for CTR Models: Kernel Fusion, Multi‑Stream Execution, and Batch Merging

By fusing sparse‑feature operators, enabling multi‑stream execution, consolidating data copies, and merging inference batches, iQIYI reduced GPU CTR model latency to CPU‑level, boosted throughput over sixfold, and cut operational costs by more than 40%, overcoming launch‑overhead bottlenecks.

GPUKernel FusionTensorFlow

0 likes · 10 min read

Optimizing GPU Inference for CTR Models: Kernel Fusion, Multi‑Stream Execution, and Batch Merging

Baidu Geek Talk

Jan 15, 2024 · Artificial Intelligence

Qianfan Large Model Platform: Making Large Models Accessible - Baidu's Latest Work on Model Fine-tuning and Deployment

Baidu’s Qianfan Large Model Platform provides a one‑stop enterprise solution with 54 pre‑installed models, advanced fine‑tuning, comprehensive evaluation metrics, and optimized deployment that cuts costs up to 90% and boosts throughput 3‑5×, enabling rapid, affordable AI application development.

AI Native ApplicationsBaidu QianfanCost Optimization

0 likes · 12 min read

Qianfan Large Model Platform: Making Large Models Accessible - Baidu's Latest Work on Model Fine-tuning and Deployment

Alimama Tech

Nov 2, 2022 · Artificial Intelligence

Optimizing GPU Utilization for Multimedia AI Services with high_service

The article presents high_service, a high‑performance inference framework that boosts GPU utilization in multimedia AI services by separating CPU‑heavy preprocessing from GPU inference, employing priority‑based auto‑scaling, multi‑tenant sharing, and TensorRT‑accelerated models to eliminate GIL bottlenecks, reduce waste, and adapt to fluctuating traffic, with future work targeting automated bottleneck detection and further CPU‑GPU offloading.

GPU utilizationHigh Performance ComputingTensorRT

0 likes · 19 min read

Optimizing GPU Utilization for Multimedia AI Services with high_service

DataFunSummit

Apr 19, 2022 · Artificial Intelligence

DeepSpeed‑MoE: End‑to‑End Training and Inference Solutions for Mixture‑of‑Experts Models

This article reviews DeepSpeed‑MoE, an end‑to‑end system that introduces new MoE architectures, model‑compression techniques, and highly optimized inference pipelines, detailing its motivation, design of PR‑MoE (Pyramid‑MoE and Residual‑MoE), distributed parallel strategies, communication and kernel optimizations, and performance gains over dense baselines.

AIDeepSpeedMixture of Experts

0 likes · 11 min read

DeepSpeed‑MoE: End‑to‑End Training and Inference Solutions for Mixture‑of‑Experts Models

DataFunTalk

Dec 25, 2020 · Artificial Intelligence

Exploring Pretraining Model Optimization and Deployment Challenges in NLP

This article reviews the evolution of pretraining models in NLP, discusses the practical challenges of deploying large models such as inference latency, knowledge integration, and task adaptation, and presents Xiaomi’s optimization techniques including knowledge distillation, low‑precision inference, operator fusion, and multi‑granularity segmentation for dialogue systems.

BERTNLPPretraining

0 likes · 15 min read

Exploring Pretraining Model Optimization and Deployment Challenges in NLP