AI infrastructure – Page 2

This AI Paper from ByteDance Introduces MegaScale-Infer: A Disaggregated Expert Parallelism System for Efficient and Scalable MoE-Based LLM Serving

Large language models are built on transformer architectures and power applications like chat, code generation, and search, but their growing […]

Scalable and Principled Reward Modeling for LLMs: Enhancing Generalist Reward Models RMs with SPCT and Inference-Time Optimization

Reinforcement Learning RL has become a widely used post-training method for LLMs, enhancing capabilities like human alignment, long-term reasoning, and […]

UB-Mesh: A Cost-Efficient, Scalable Network Architecture for Large-Scale LLM Training

As LLMs scale, their computational and bandwidth demands increase significantly, posing challenges for AI training infrastructure. Following scaling laws, LLMs […]

This AI Paper Unveils a Reverse-Engineered Simulator Model for Modern NVIDIA GPUs: Enhancing Microarchitecture Accuracy and Performance Prediction

GPUs are widely recognized for their efficiency in handling high-performance computing workloads, such as those found in artificial intelligence and […]

PilotANN: A Hybrid CPU-GPU System For Graph-based ANNS

Approximate Nearest Neighbor Search (ANNS) is a fundamental vector search technique that efficiently identifies similar items in high-dimensional vector spaces. […]

NVIDIA AI Open Sources Dynamo: An Open-Source Inference Library for Accelerating and Scaling AI Reasoning Models in AI Factories

The rapid advancement of artificial intelligence (AI) has led to the development of complex models capable of understanding and generating […]

Category: AI infrastructure