#datacenterinferencing resultados de búsqueda

Today Thinking Machines Lab is launching our research blog, Connectionism. Our first blog post is “Defeating Nondeterminism in LLM Inference” We believe that science is better when shared. Connectionism will cover topics as varied as our research is: from kernel numerics to…

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing…

Data centers are evolving to meet AI-driven power and cooling demands. Join Critical Digital Infrastructure community for technical insights and discussions on AI infrastructure challenges: ms.spr.ly/6016tJHBQ #Vertiv

Scaling LLM inference is not an ML problem. It’s a distributed systems problem in disguise. And if you plan to work as an AI Engineer soon, you'd want to know how to solve this problem. 1. Batching ≠ Throughput — it’s a scheduling problem. When hundreds of inference requests…

$IREN - According to Deloitte, AI Data Center Power Demand Could Surge 30x by 2035.

Why Scale-Out #DataCenter Architecture Falls Short in the Age of #AI - buff.ly/ELqpDaG #GenaI #ML #IT #ITinfrastructure

New for your AI infrastructure → goo.gle/4nJPmet Ironwood TPUs: Purpose-built for high-performance inference with 10x performance over TPU v5p and >4x over TPU v6e. New Axion VMs - N4A & C4A metal: Redefining price-performance for the general-purpose workloads.

ICYMI: The NVIDIA AI Factory for Government reference design provides guidance for full-stack deployments in the public sector and highly regulated industries. Backed by NVIDIA's trusted infrastructure, it's designed to power innovation across industries. Learn more ➡️…

$TPL Data centers? Texas Pacific Land just added data centers to the thesis, as it brought up conversations with hyperscalers and potential "news to share here in the very near future." --> It would certainly fit the Permian DC thesis. ---> It would be quite funny if TPL were…

Tomorrow, we have the next installment of our @Cambridge_Uni ML Systems Seminars (@CaMLSys). Friday Nov 7th at 11am, we are happy to have Finn Anderson presenting "DISCO: DYNAMICAL INTEGRATION SYSTEMS FOR CONVERGENCE OPTIMISATION IN DISTRIBUTED LOW-COMMUNICATION TRAINING". Join…

The data center industry is shifting! Tax policy is emerging as a critical factor, with government structures, ownership model taxation, and trade rules impacting equipment costs. Dive into our latest analysis to navigate this maturing market. bit.ly/3Xd6w9x

We just put out a key step for making distributed training work at larger and larger models: Scaling Laws for DiLoCo TL;DR: We can do LLM training across datacenters in a way that scales incredibly well to larger and larger models!

Have you heard the news? A new learning pathway has dropped in partnership with @NVIDIAAIDev via the #GoogleDeveloperProgram. ✨ Learn the fundamentals of AI inference and how you can run and optimize them on GPUs in @GoogleCloud for peak performance → goo.gle/4hQKbs8

AMD Instinct MI355X was supposed to compete with NVIDIA Blackwell right? So much for AMD having an advantage in inference.

📣 NVIDIA Blackwell sets the standard for AI inference on SemiAnalysis InferenceMAX. Our most recent results on the independent benchmarks show NVIDIA’s Blackwell Platform leads AI factory ROI—— see how NVIDIA Blackwell GB200 NVL72 can yield $75 million in token revenue over…

We are excited to share a new milestone — we've open-sourced dInfer, a high-performance inference framework for diffusion language models (dLLMs). 🚀10.7X speedup over NVIDIA’s diffusion model framework Fast-dLLM. 🧠1,011 tokens per second in single-batch inference — on the…

Architecture of a Data Centre White space: This refers to the area where IT equipment is placed, including servers, storage, network gear, racks, air conditioning units, and power distribution systems/units (PDUs) Grey space: This refers to the area where back-end…

New blog post! We can use inference-time compute to reduce hallucination rates in reasoning models by injecting an interruption token and sampling in parallel.(1/n)

Deterministic inference: getting the exact same output every time you run an LLM with identical inputs. Try it yourself: deterministicinference.com Powered by EigenAI @eigenlayer