Tether Brings AI Memory Compression To Consumer Devices

Tether’s TurboQuant enables useful and powerful local AI applications on consumer devices at much lower costs and without ...
Hosted on MSN

Google says TurboQuant cuts LLM KV-cache memory use 6x, boosts speed

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...
VentureBeat

New KV cache compaction technique cuts LLM memory 50x without accuracy loss

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 ...
Semiconductor Engineering

Dynamic KV Cache Scheduling in Heterogeneous Memory Systems for LLM Inference (Rensselaer Polytechnic Institute, IBM)

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 ...
EDN

MRAM technologies: from space applications to unified cache memory?

Magneto-resistive random access memory (MRAM) is a non-volatile memory technology that relies on the (relative) magnetization state of two ferromagnetic layers to store binary information. Throughout ...