Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO
Elastic GPUs, sandbox growth, and the economics of the agent cloud
AI NEWS JOURNAL
All AI updates
Explore the complete news stream by source, topic and date.
Jas Kaira, Head of AI Business at Blackstone: Concerns about AI infrastructure debt are exaggerated.
Jas Kaira, head of Blackstone’s AI business, stated at the TechCrunch Disrupt conference that market panic over massive debt in the AI infrastructure sector has been overly amplified. His key argument is that this debt is largely backed by long-term contracts with high‑credit‑rating clients—such as Microsoft and Meta—and that the underlying assets (e.g., GPUs) possess strong collateral value, meaning the risk profile differs fundamentally from that of the 2008 banking system.
NVIDIA cuDSS officially released: Accelerating the solution of sparse linear systems in engineering and scientific simulations
On October 26, 2026 (UTC), NVIDIA announced that the cuDSS library has entered General Availability (GA). This CUDA-X library is specifically designed to efficiently solve large-scale sparse linear systems on GPUs, significantly boosting simulation throughput and iterative efficiency across fields such as chip design, aerospace, and industrial systems.
TorchNeuron System-Level Performance Analysis (Part 1): End-to-End Profiling Based on the AWS Trainium Chip
This article is the first in a series of technical blog posts jointly published by Hugging Face and Amazon, systematically introducing how to use TorchNeuron on AWS Trainium AI accelerator chips for PyTorch model inference performance analysis. It covers the principles of Neuron backend integration, methods for configuring torch.profiler, the use of NeuronConfig and NeuronProfiler tools, and visualized tracing with Neuron Explorer. Additionally, using RMSNorm as an example, it compares the device‑time differences among three implementations: plain PyTorch, the built-in torch.rms_norm fusion, and torch.compile’s automatic fusion.
i14 releases the Laplace 3B linear attention model: inference with fixed-state memory, implemented on AWS Trainium2 and native PyTorch.
i14 has launched the Laplace 3B model with 3.028 billion parameters, which replaces Softmax attention with Gated Linear Attention (GLA). During inference, its per-sequence recursive state remains constant at 29.5 MB, independent of context length. The model was trained and validated on a single AWS trn2.48xlarge instance (equipped with 16 Trainium 2 chips), achieving a training throughput of 120,000 tokens per second and an inference throughput of 11,544 output tokens per second (with 1,280 concurrent sequences). All performance metrics are based on internal measurements conducted by i14 and the AWS GenAIIC team.
SF Compute builds a programmable data center: leveraging physical delivery to power the GPU computing power trading market.
SF Compute was founded by Evan Conrad and is dedicated to building a neutral, programmable GPU computing‑power trading infrastructure that supports physical delivery. Its model rejects the abstraction of FLOPS as a commodity, emphasizing hardware heterogeneity, actual delivery, and real‑time constraints—such as interrupt tolerance, InfiniBand topology, and firmware versions. The platform has already facilitated tens of billions of dollars in transaction volume and introduced low‑level capabilities like dynamic InfiniBand subnet partitioning.
Visiting Samsung’s Paju Ultra-Large-Scale Semiconductor New Facility: The AI-Driven Surge in Memory Chip Capacity Expansion
To meet the surging demand for high-bandwidth memory chips driven by the explosive growth of generative AI, Samsung is building a semiconductor manufacturing cluster in Pyeongtaek, South Korea, spanning an area equivalent to New York’s Central Park. To date, three fabs have been commissioned, with a fourth set to begin production soon, and construction on a fifth—halted in 2024 due to declining demand—has now resumed. The site showcases an intense construction pace and a workforce ecosystem adhering to stringent safety standards.
Hugging Face launches TorchTPU: PyTorch natively supports Google TPU
Hugging Face has released TorchTPU, providing native TPU backend support for PyTorch, enabling libraries such as Transformers and Diffusers to run directly on TPUs without relying on torch_xla or migrating to JAX.
TorchNeuron launches public beta: Hugging Face models natively support AWS Trainium for training and inference.
Hugging Face, in collaboration with AWS, has launched TorchNeuron—a PyTorch-native Trainium backend—available for public beta starting October 20, 2026. This technology enables mainstream libraries such as Transformers and Diffusers to run directly on Trainium chips without any modifications, significantly enhancing the usability and compatibility of text-generation and image-generation models.
Impactful scheduling for GPU clusters
Impactful scheduling for GPU clusters
Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
Differential acceleration of cyber, math, and AI
GPU Management: Why Idle GPUs Are the New Grounded Aircraft
GPU Management: Why Idle GPUs Are the New Grounded Aircraft
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction. We face a new epoch in computing. Hardware is changing rapidly — not just faster GPUs, but a growing range of chips from different vendors, each with its own architecture and often tailored to specific AI workloads. Software is changing just as fast, and AI coding tools now generate in minutes what took months of effort a few years ago. With so much of computing now centered on AI, GPU kernels are a crucial component of its success. These are the low-level programs that run inside the GPU, and writing efficient ones is far from obvious — it takes years of expertise to get right. Transferring a kernel from o
Import AI 464: Fable writes GPU kernels; AI automation; and analog computation
Is this the beginning of a new world?
Import AI 463: Self-improving robots; a 10k Chinese GPU cluster; and an elegiac essay for the human era
What eras bookend our interregnum?