FengHuang: Next-Generation Memory Orchestration for AI Inferencing

2025-11-13

AI Summary

Traditional GPU architectures face fundamental limitations in memory capacity, bandwidth, and interconnect scalability, hindering efficient large-model inference. To address this, we propose a decoupled AI inference infrastructure featuring a novel multi-level shared memory architecture that integrates high-speed local memory with centralized remote memory. Our design incorporates tensor-granularity proactive paging, near-memory computing, and high-bandwidth interconnects to enable elastic coordination between memory and compute resources. Evaluated on GPT-3, Grok-1, and Qwen3-235B, the system achieves up to 93% local memory savings, a 50% reduction in GPU utilization, and 16–70× acceleration in cross-GPU communication. These improvements significantly enhance inference throughput and reduce deployment costs, establishing a scalable foundation for next-generation large-language-model serving.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This document presents a vision for a novel AI infrastructure design that has been initially validated through inference simulations on state-of-the-art large language models. Advancements in deep learning and specialized hardware have driven the rapid growth of large language models (LLMs) and generative AI systems. However, traditional GPU-centric architectures face scalability challenges for inference workloads due to limitations in memory capacity, bandwidth, and interconnect scaling. To address these issues, the FengHuang Platform, a disaggregated AI infrastructure platform, is proposed to overcome memory and communication scaling limits for AI inference. FengHuang features a multi-tier shared-memory architecture combining high-speed local memory with centralized disaggregated remote memory, enhanced by active tensor paging and near-memory compute for tensor operations. Simulations demonstrate that FengHuang achieves up to 93% local memory capacity reduction, 50% GPU compute savings, and 16x to 70x faster inter-GPU communication compared to conventional GPU scaling. Across workloads such as GPT-3, Grok-1, and QWEN3-235B, FengHuang enables up to 50% GPU reductions while maintaining end-user performance, offering a scalable, flexible, and cost-effective solution for AI inference infrastructure. FengHuang provides an optimal balance as a rack-level AI infrastructure scale-up solution. Its open, heterogeneous design eliminates vendor lock-in and enhances supply chain flexibility, enabling significant infrastructure and power cost reductions.

Problem

• Addressing GPU memory limitations in large language model inference workloads
• Overcoming interconnect scaling bottlenecks for distributed AI inference systems
• Providing scalable disaggregated memory architecture to reduce GPU resource requirements

Innovation

• Multi-tier shared-memory architecture with disaggregated remote memory
• Active tensor paging and near-memory compute optimization
• Rack-level scale-up solution eliminating vendor lock-in