Generative AI applications don’t need bigger memory, but smarter forgetting. When building LLM apps, start by shaping working memory. You delete a dependency. ChatGPT acknowledges it. Five responses ...
Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...
A new technical paper titled “Hardware-based Heterogeneous Memory Management for Large Language Model Inference” was published by researchers at KAIST and Stanford University. “A large language model ...
A new technical paper titled “Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System” was published by researchers at Rensselaer Polytechnic Institute and IBM. “Large ...
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New AI techniques slash LLM memory use and costs
TurboQuant breakthrough: Google's TurboQuant compresses LLM KV-cache up to 6x without quality loss, freeing GPU memory and boosting inference speed. Hybrid attention savings: DeltaNet-style ...
Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working ...
Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...
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