As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in production inference systems. For a ...

Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...

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 ...

Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and environmental states, ...

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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 ...

Maru is a high-performance KV cache storage engine built on CXL shared memory, designed for LLM inference scenarios where multiple instances need to share a KV cache with minimal latency. Every ...

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 ...

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 ...