Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill

Because there's only one reality to model! The post How Major Reasoning Models Converge to the Same “Brain” as They Model Reality Increasingly Better appeared first on Towards Data Science.

Late last year I got pulled into a capacity planning exercise for a global retailer that had wired a 70B model into their product search and recommendation pipeline. Every search query triggered an inference call. During holiday traffic their cluster was burning through GPU-hours at a rate that made their cloud finance team physically uncomfortable. They had already scaled from 24 to 48 H100s and latency was still spiking during peak hours. I was brought in to answer a simple question: Do we need 96 GPUs for the January sale or is something else going on? I started where I always start with these engagements: profiling. I instrumented the serving layer and broke the utilization data down by inference phase. What came back changed how I think about GPU infrastructure. During prompt processing — the phase where the model reads the entire user input in parallel — the H100s were running at 92% compute utilization. Tensor cores fully saturated. Exactly what you want to see on a $30K GPU. Bu

A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.

When a major insurer’s AI system takes months to settle a claim that should be resolved in hours, the problem usually isn’t the model in isolation. It’s the system around the model and the latency that system introduces at every step. Speed in enterprise AI isn’t about impressive benchmark numbers. It’s about whether AI can... The post AI latency is a business risk. Here’s how to manage it appeared first on DataRobot.

Most people familiar with generative models know that LLMs are trained on the entirety of the internet’s content. Many regard their millions of parameters and hyperparameters, which dwarf the quantity […] The post Training Isn’t Enough: Reasoning Models and LLMs Need Reinforcement Learning appeared first on AIwire.

Reid Hoffman has expressed support for tracking AI usage through token consumption, as debate grows across the tech industry over how to measure effective adoption of artificial intelligence tools. His comments follow recent scrutiny of internal tracking practices at Meta, which had monitored employee AI usage before shutting down a related dashboard after leaks. Tokens, […]

Deployment is not just about calling an API or hosting a model. It involves decisions around architecture, cost, latency, safety, and monitoring.

You approved the business case. The pilot showed promise. Then production changed the math. Agentic AI doesn’t just cost what you build. It costs what it takes to run, govern, evaluate, secure, and scale. Most enterprises don’t model those operating costs clearly until they are already absorbing them. Expenses compound fast. Token usage grows with... The post Agentic AI costs more than you budgeted. Here’s why. appeared first on DataRobot.