newsIEEE Spectrum AITrust 88 · LabPublished 1mo agoLive · 5d ago
New Server Hopes to Break Through AI’s “Memory Wall”
Memory is arguably the most serious constraint on modern AI large language models (LLMs). According to one influential paper , LLM token generation is an inherently memory-bound task, meaning the rate at which models output text is lim
Covers
Covers (incoming)
paperScaling limit of the Random Language ModelpaperFrom Tokens to States: LLMs as a Special Case of World Models and the Continuous Path BeyondpaperSelective Memory Retention for Long-Horizon LLM AgentspaperThe Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth ScalingpaperEvolution Fine-Tuning: Learning to Discover Across 371 Optimization TaskspaperMulti-Block Diffusion Language ModelspaperRepresentational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language ModelspaperAttend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM InferencepaperSurrogate Fidelity: When Can Open LLMs Explain Closed Ones?paperCHERRY: Compressed Hierarchical Experts with Recurrent Representational YieldpaperUnderstanding Large Language Modelsrepoplur-ai/plurrepomem0ai/mem0repoAI-Hypercomputer/maxtextrepomanojmallick/sigmaprepojordanhubbard/nanolangrepogaran0613/ai-memory-gateway
Related across the graph
paperEvolution Fine-Tuning: Learning to Discover Across 371 Optimization TaskspaperMulti-Block Diffusion Language ModelspaperThe Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scalingrepomem0ai/mem0paperCHERRY: Compressed Hierarchical Experts with Recurrent Representational Yieldrepogaran0613/ai-memory-gatewaypaperRepresentational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language ModelspaperCARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear AttentionpaperSparse attention at million-token contextpaperAttend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM InferencepaperSurrogate Fidelity: When Can Open LLMs Explain Closed Ones?paperUnderstanding Large Language ModelspaperSelective Memory Retention for Long-Horizon LLM AgentspaperScaling limit of the Random Language Modelrepojordanhubbard/nanolangrepoplur-ai/plurrepoAI-Hypercomputer/maxtextpaperFrom Tokens to States: LLMs as a Special Case of World Models and the Continuous Path BeyondrepoNoshkoto/Noshyrepomanojmallick/sigmappaperSpeculative decoding with draft models