Beyond Market Intelligence/Workload

Workload

Workload 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 workload 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 workload, 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.

What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]
Machine Learning

What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]

Struggling with persistent machine learning bottlenecks? GPU Programming with Triton, now in early access from Manning, offers a practical pathway to accelerating training and inference by crafting custom GPU kernels—all within Python. The book guides you through identifying optimization opportunities, benchmarking kernels, and leveraging techniques like tiling and vectorization. Triton empowers practitioners to move beyond framework limitations when a model demands more. Explore how you might accelerate your workload—and what currently holds you back.

Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them
Towards Data Science

Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them

For two decades, autoscaling has been a cornerstone of cloud infrastructure. However, the rise of agentic traffic—autonomous agents dynamically generating requests—is exposing fundamental limitations in these established approaches. This post explores three generations of autoscaling and definitively demonstrates how agentic traffic renders them ineffective. Discover a new paradigm for capacity planning, one built to address the evolving demands of the AI era. For further insight into related infrastructure investments, see "Nvidia investing $1.5B in SoftBank data center developer behind OpenAI project."

Machine Learning

If you had a bunch of GPUs lying around, what would you actually build with them? (Running LLMs is off the table) [D]

Beyond the well-trodden path of local LLMs, a stack of high-end GPUs unlocks a realm of compelling possibilities. What truly innovative projects would emerge? Consider distributed simulations, specialized generative models outside of text, or accelerated rendering pipelines. The opportunity exists for impactful homelab experiments demanding serious computational power, or even uniquely ambitious personal endeavors. Explore the potential – as demonstrated by projects like the Doom renderer reimagined as a transformer, discussed in "I compiled Doom's renderer into a 21B-parameter transformer"—and share your most intriguing ideas.

12 Ways to Reduce LLM Latency and Inference Costs in Production
KDnuggets

12 Ways to Reduce LLM Latency and Inference Costs in Production

Scaling large language models (LLMs) effectively moves beyond simply adding more GPUs. It demands a rigorous focus on optimizing request efficiency. This article details 12 proven strategies to reduce LLM latency and inference costs in production environments. Ranked by impact, these methods address wasted work within each request—from caching and quantization to optimized prompting and batching. Discover practical techniques to empower your LLM deployments and maximize performance.