
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
Etched is an AI chip startup founded by three Harvard dropouts that has raised $300 million in Series C funding at a $10.3 billion valuation, doubling its valuation in seven months. The company designed specialized chips and memory technology to speed up AI inference, the computing process that happens after a user submits a prompt. Etched faced significant skepticism about its approach of building chips specifically for transformer-based AI models, but has since demonstrated that its systems can run various AI architectures and has booked $1 billion in orders. The funding round was led by major investors including Sequoia and Andreessen Horowitz, along with other backers who tested the hardware directly.

In this tutorial, we explore TileLang as a high-level Python domain-specific language for designing and compiling performance-oriented GPU kernels through TVM. We begin by validating the CUDA environment and establishing reusable benchmarking and numerical-verification utilities, then progressively implement vector addition, tiled tensor-core matrix multiplication, schedule exploration, fused GEMM epilogues, row-wise softmax, and FlashAttention. Throughout the tutorial, we work directly with Ti

A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.

As AI infrastructure grows more complex, companies are rethinking how they acquire, own, and finance assets that operate on dramatically different economic timelines.
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