GPU memory
GPU memory on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on gpu memory in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around gpu memory, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management
Modern Large Language Models (LLMs) demand optimized Key-Value (KV) cache management to unlock peak performance. As context windows expand, GPU memory consumption becomes a critical bottleneck, impacting concurrency and latency. Two significant advancements address this challenge: PagedAttention refines memory allocation, while RadixAttention facilitates efficient prefix reuse. These techniques collectively enable substantial gains in LLM throughput. Explore the details of these breakthroughs and their impact on production LLMs in our full post, building upon insights from experiences like "The LLM Judge That Kept Agreeing With Itself."
Qwen3.8-27B runs frontier-class coding agents and reasoning locally, no cloud API required
Alibaba's Qwen3.8-27B model marks a significant shift in the AI landscape, offering frontier-class coding and reasoning capabilities accessible locally—no cloud API required. This 27-billion-parameter model, released under an open-source license, delivers impressive performance, rivaling proprietary models like Claude Opus on key benchmarks. Its compact size, runnable on consumer hardware, empowers developers and enterprises to explore AI-driven solutions with greater privacy, control, and cost-efficiency, fundamentally changing how powerful AI can be deployed.
When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop
The future of management is rapidly evolving. Within five to ten years, your company’s most effective leader might be an AI agent, operating continuously within shared GPU memory. This shift represents a systems-level transformation – the algorithmic corporation – where middle management protocols emerge and current AI limitations are addressed. Explore how autonomous agents can fundamentally reshape business operations. For deeper insights into the cost implications of multi-agent architectures, see our article, "The 3× Token Bill We Didn’t See Coming."
Stop adding more GPUs: Weka's new storage platform reduces load by caching 100% of an AI model's pre-calculated tokens
GPU memory is rapidly becoming the primary bottleneck in production AI, particularly as models demand longer context windows. Weka’s new storage platform directly addresses this challenge, offering a transformative approach that extends GPU capacity with cost-effective flash storage. Through its NeuralMesh 6 software and Wekapod 3 hardware, Weka’s Augmented Memory Grid caches 100% of pre-calculated tokens, eliminating redundant computations and significantly reducing inference costs.