Vienna startup Ora Computing raised €3.5M and proved a 70-billion-parameter large language model can be compressed for under ...

Nota AI, a company specializing in AI model compression and optimization, announced that two of its papers on MoE-specific ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...

Tether successfully integrated Google’s TurboQuant into the inference engine of its local AI framework, QVAC. It is the ...

Using special tags embedded in the output, the model directly links every factual claim it makes to the specific source document or database row it pulled the information from.

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

You can now download Gemma 4 models with quantization-aware training to reduce the amount of mobile memory required to 1GB.