#gpu-memory News & Analysis

4 articles tagged with #gpu-memory. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Apr 147/10

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AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM

Researchers introduce AtlasKV, a parametric knowledge integration method that enables large language models to leverage billion-scale knowledge graphs while consuming less than 20GB of VRAM. Unlike traditional retrieval-augmented generation (RAG) approaches, AtlasKV integrates knowledge directly into LLM parameters without requiring external retrievers or extended context windows, reducing inference latency and computational overhead.

AIBullisharXiv – CS AI · Mar 117/10

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ARKV: Adaptive and Resource-Efficient KV Cache Management under Limited Memory Budget for Long-Context Inference in LLMs

Researchers propose ARKV, a new framework for managing memory in large language models that reduces KV cache memory usage by 4x while preserving 97% of baseline accuracy. The adaptive system dynamically allocates precision levels to cached tokens based on attention patterns, enabling more efficient long-context inference without requiring model retraining.

AIBullisharXiv – CS AI · Mar 47/103

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Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving

Nightjar is a new adaptive speculative decoding framework for large language models that dynamically adjusts to system load conditions. It achieves 27.29% higher throughput and up to 20.18% lower latency by intelligently enabling or disabling speculation based on workload demands.

AINeutralHugging Face Blog · Dec 244/106

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Visualize and understand GPU memory in PyTorch

The article appears to be a technical guide focused on visualizing and understanding GPU memory usage in PyTorch, a popular machine learning framework. This type of content typically helps developers optimize their AI model training and deployment by better managing memory resources.