venturebeat
Breaking through AI’s memory wall with token warehousing

As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into focus: memory. Not compute. Not models. Memory.Under the hood, today’s GPUs simply don’t have enough space to hold the Key-Value (KV) caches that modern, long-running AI agents depend on to maintain context. The result is a lot of invisible waste — GPUs redoing work they’ve already done, cloud costs climbing, and performance taking a hit. It’s a problem that’s already showing up in production environments, even if most people haven’t named it yet.At a recent stop on the VentureBeat AI Impact Series, WEKA CTO Shimon Ben-David joined VentureBeat CEO Matt Marshall to unpack the industry’s emerging “memory wall,” and why it’s becoming one of the big [...]

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venturebeat
Nvidia says it can shrink LLM memory 20x without changing model weights

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 mo [...]

Match Score: 112.17

venturebeat
How DeepSeek’s radical architecture is shattering Silicon Valley's token moat

DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]

Match Score: 110.23

venturebeat
5% GPU utilization: The $401 billion AI infrastructure problem enterprises can't keep ignoring

For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity [...]

Match Score: 97.83

venturebeat
A 0.12% parameter add-on gives AI agents the working memory RAG can't

AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]

Match Score: 94.37

venturebeat
DeepSeek’s conditional memory fixes silent LLM waste: GPU cycles lost to static lookups

When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access stati [...]

Match Score: 75.89

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 me [...]

Match Score: 75.47

venturebeat
Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a [...]

Match Score: 71.56

venturebeat
Google PM open-sources Always On Memory Agent, ditching vector databases for LLM-driven persistent memory

Google senior AI product manager Shubham Saboo has turned one of the thorniest problems in agent design into an open-source engineering exercise: persistent memory.This week, he published an open-sour [...]

Match Score: 71.11

venturebeat
The attack dominating financial services doesn't steal passwords. It resets MFA and steals the token.

The attacker who hit the most financial services organizations over the past 12 months never phished a password. They called an IT support line, convinced an employee to reset their MFA, and registere [...]

Match Score: 66.20