According to DeepLearning.AI on Twitter, a new short course in collaboration with Qdrant introduces AI professionals to advanced multi-vector image retrieval techniques. Led by Senior Developer ...

Explore the significance of model quantization in AI, its methods, and impact on computational efficiency, as detailed by NVIDIA's expert insights. As artificial intelligence (AI) models grow in ...

The deployment of Spiking Neural Networks (SNNs) on resource-constrained edge devices is hindered by a critical algorithm-hardware mismatch: a fundamental trade-off between the accuracy degradation ...

Huawei’s Zurich Computing Systems Laboratory has released SINQ (Sinkhorn Normalization Quantization), an open-source quantization method that reduces the memory requirements of large language models ...

New capabilities deliver up to 5X faster filtered vector search, improved ranking quality, and lower infrastructure costs to unlock scalable, cost-efficient AI applications SAN FRANCISCO--(BUSINESS ...

New capabilities deliver up to 5X faster filtered vector search, improved ranking quality, and lower infrastructure costs to unlock scalable, cost-efficient AI applications “We’re committed to giving ...

This project aims to integrate BBQ into the OpenSearch k-NN plugin to offer users a memory-efficient alternative, ideal for large-scale vector workloads in constrained compute environments. The ...

LLMs show impressive capabilities across numerous applications, yet they face challenges due to computational demands and memory requirements. This challenge is acute in scenarios requiring local ...