GPU utilization
GPU utilization on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on gpu utilization 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 utilization, 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.
VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push
VentureBeat significantly expands its enterprise AI research capabilities with the appointment of Rob Strechay as its first Lead Analyst. Strechay, formerly of theCUBE Research, brings three decades of experience across practitioner, executive, and analyst roles, uniquely positioning him to address the critical data needs of technical decision-makers. His focus will initially encompass cloud infrastructure, data infrastructure, and AI security, complementing VentureBeat’s VB Pulse surveys—including recent findings on agentic orchestration—to provide objective insights for navigating the evolving AI landscape.
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
Enterprises are rapidly accelerating investment in AI infrastructure, yet a significant "compute gap" exists – heavy spending outpacing the ability to truly understand and control its economics. New VentureBeat Pulse Research, surveying 107 organizations, reveals that while only 21% run AI at scale, nearly half intend to evaluate specialized AI clouds within the year, often lacking clear visibility into GPU utilization (83% below 50%) and compute costs.
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.
PyTorch model running 170x slower on T4 vs A100. What could cause a bottleneck this extreme? [D]
A recent report highlights a stark performance disparity: a PyTorch model experienced a 170x slowdown when running on an NVIDIA T4 versus an A100 GPU. This extreme bottleneck, observed with a point-tracking model processing 47 frames at 256x256 resolution, suggests factors beyond typical generational hardware differences. With 99% GPU utilization and pure FP32 precision, potential causes include inefficient 4D correlation volume calculations or transformer layer performance. Further profiling is recommended to pinpoint the specific bottleneck.
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
Enterprises are accelerating AI infrastructure spending, yet visibility into its economics lags significantly—a phenomenon we've termed the "compute gap." Across 107 organizations, intentions to evaluate specialized AI clouds are surging, even as existing GPUs sit at half utilization or less, and fewer than half rigorously track compute costs. This reveals a disconnect: organizations are buying more infrastructure faster than they can account for what they already own, signaling a shift away from traditional hyperscalers.