Google’s new compression algorithm cut memory stocks within hours of publication
Google published a research blog post on Tuesday about a new compression algorithm for AI models. Within hours, memory stocks were falling. Micron dropped 3 per cent, Western Digital lost 4.7 per cent, and SanDisk fell 5.7 per cent, as investors recalculated how much physical memory the AI industry might actually need. The algorithm is […]<br /> This story continues at The Next Web [...]
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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 [...]
Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit
Context windows are becoming a computational bottleneck. The longer an agent runs, the more tokens accumulate from retrieved documents, reasoning traces and conversation history, and the more memory a [...]
DeepSeek drops open-source model that compresses text 10x through images, defying conventions
DeepSeek, the Chinese artificial intelligence research company that has repeatedly challenged assumptions about AI development costs, has released a new model that fundamentally reimagines how large l [...]
Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more
As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache [...]
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 [...]
'Observational memory' cuts AI agent costs 10x and outscores RAG on long-context benchmarks
RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, thos [...]
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 [...]
Tencent's Team Memory shares AI agent memory across a team — with no governance yet for when it's wrong
A VB Pulse survey this June found that 57% of enterprises had traced a confidently wrong agent answer back to missing or inconsistent context — the latest sign of how central context has become to w [...]