Changing AI math could reduce the hardware burden, researchers show

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...
GitHub

OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond

TNI is pervasive across X-LLMs. In text-only models, it manifests as low-norm outlier tokens, also known as Attention Sink tokens. In multi-modal settings, TNI exhibits more diverse forms, including ...
Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

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 ...
Semiconductor Engineering

Balancing Training, Quantization, And Hardware Integration In NPUs

Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor ...
theregister

Honey, I shrunk the LLM! A beginner's guide to quantization – and testing it

HANDS ON If you hop on Hugging Face and start browsing through large language models, you'll quickly notice a trend: Most have been trained at 16-bit floating point of Brain-float precision. FP16 and ...