Xiaomi MiMo and TileRT Push a 1-Trillion-Parameter Model Past 1000 Tokens Per Second on Commodity GPUs

Xiaomi's MiMo-V2.5-Pro-UltraSpeed blows past the speed threshold custom silicon companies spent years building toward—on regular GPUs.

Google released the Colab CLI, letting developers and AI agents run local code on remote Colab GPU and TPU runtime The post Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal appeared first on MarkTechPost.

SpaceX has secured a major compute agreement withGoogle ahead of its planned Nasdaq listing, adding another large customer to its expanding AI infrastructure business. A regulatory filing by SpaceX said Google will pay the company $920 million per month from…

I set up an AI agent on a rented GPU, pointed it at a training script, and went to bed. By morning it had run 40 experiments, improved validation loss by 5.9%, and cut memory usage from 44 GB to 17 GB. It also spent four hours chasing a bug that a linter introduced behind […]

A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science.

The scrutiny over Nvidia's deal highlights potential risks in financial engineering, impacting investor trust and retiree security. The post Nvidia faces scrutiny over $5.4B GPU sale to Valor amid Burry’s claims of round-tripped capital appeared first on Crypto Briefing.

The post NVIDIA Launches DynoSim for Efficient AI Serving Optimization appeared on BitcoinEthereumNews.com. Felix Pinkston May 29, 2026 23:09 NVIDIA’s DynoSim accelerates AI model deployment by simulating the Pareto frontier for workloads, cutting GPU costs and boosting efficiency. NVIDIA has unveiled DynoSim, a simulation tool designed to optimize large language model (LLM) deployments by mapping the Pareto frontier for workload configurations. The tool, announced on May 29, 2026, promises to reduce GPU costs and streamline infrastructure planning for AI serving at scale. Modern LLM serving is notoriously complex, involving interdependent variables like tensor-parallel configurations, cache behavior, scheduler settings, and autoscaling thresholds. Testing these setups in real-world environments is both time-consuming and expensive. This is where DynoSim steps in, acting as a discrete-event simulator that replicates NVIDIA’s Dynamo AI serving stack at atomic granulari

Back-to-back price cuts from China's top AI labs have made their models a fraction of the cost of GPT-5.5 and Claude Opus.