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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 ...

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 ...
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

Adaptive RISC-V Cache Architecture for Near-Memory Extensions (Politecnico di Torino, EPFL)

“Modern data-driven applications expose limitations of von Neumann architectures – extensive data movement, low throughput, and poor energy efficiency. Accelerators improve performance but lack ...
VentureBeat

Breaking through AI’s memory wall with token warehousing

Shimon Ben-David, CTO, WEKA and Matt Marshall, Founder & CEO, VentureBeat As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into ...
Electronic Design

Hardware Compression Works at the Memory Cache Level

How lossless data compression can reduce memory and power requirements. How ZeroPoint’s compression technology differs from the competition. One can never have enough memory, and one way to get more ...
EDN

SoC design: When a network-on-chip meets cache coherency

Many people have heard the term cache coherency without fully understanding the considerations in the context of system-on-chip (SoC) devices, especially those using a network-on-chip (NoC). To ...