arXiv (OAI Expanded)
open access
AI Hardware Accelerators for Large Language Models: Architectures and the Memory Wall
Large language models (LLMs) place unprecedented and still-growing demands on the hardware that trains and serves them. This review surveys the full landscape of AI hardware accelerators for LLMs, including general-purpose GPUs, custom ASICs such as TPUs, Trainium, Groq, and Cerebras, reconfigurable
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AI Hardware Accelerators for Large Language Models: Architectures and the Memory Wall#784815
- ThAME: 3D Memory-Enabled Heterogeneous Accelerator for LLM Mixture of Experts#386840
- On the Limitations of Non-GPU AI Accelerators for Large-Model Inference: A Field Study of MoE and Multimodal Serving on Huawei Ascend#353032
- DCO: Dynamic Cache Orchestration for LLM Accelerators through Predictive Management#998060
- AccelForge: Comprehensive Modeling and Co-Design Framework for AI Accelerators#999584
Record · ID 783225
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive