The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

As AI workloads extend across nearly every technology sector, systems must move more data, use memory more efficiently, and respond more predictably than traditional design methodologies allow. These ...

Paper: Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices (PDF) When multiple LLM agents share one local model, every new request re-computes the full ...

In an effort to work faster, our devices store data from things we access often so they don’t have to work as hard to load that information. This data is stored in the cache. Instead of loading every ...

AMD recently published a new patent that reveals that the company is working on making its 3D V-cache tech even better. Back in early 2021, we started hearing the first whispers and murmurs of a new ...

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 ...

DRAM access latency is typically 50–100 ns, which at 3 GHz corresponds to 150–300 cycles. Latency arises from signal propagation, memory controller scheduling, row activation, and bus turnaround. Each ...

Abstract: Cache memory has been introduced to accelerate embedded system performance and is automatically managed without programmer intervention through hardware-based cache controllers. However, ...