Cerebras stock falls as a building shortage bites

Cerebras posted strong Q1 results — revenue up 92% to $193.4m and a narrowed net loss — yet its stock fell ~10% after the company warned gross margins would drop sharply from 46.5% to 36–38% in Q2. The culprit is not chip supply but a shortage of data-centre space and power. Cerebras is renting back its own systems and building capacity at speed, costs that will shave 10–15 margin points this year. CEO Andrew Feldman called it a 'grand irony' that buildings, not chips, are now the limiting factor. The company guided full-year revenue of $855–865m, above analyst estimates, and highlighted a $20bn+ OpenAI inference deal and a 178% jump in cloud/services revenue. Still, the stock has fallen ~28% from its post-IPO peak, weighed down by high expectations, a broader chip-sector sell-off, and concentration risk around a few large customers.

Nguồn: https://thenextweb.com/news/cerebras-earnings-margin-data-centre-shortage. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

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PyTorch018 phút6 giờ trước

TokenSpeed-Kernel: Portable APIs and High-Performance Kernels for Multi-Silicon LLM Inference – PyTorch

TokenSpeed-kernel is an open-source, standalone subsystem that provides a clean layered API and registry system for LLM inference kernels across multiple hardware backends. It decouples the high-level runtime from hardware-specific kernel implementations using a decorator-based registration system where kernels declare their platform capabilities, tensor format signatures, and priorities. The selector then dispatches to the best available implementation at runtime. Using GPT-OSS 120B on AMD MI355X (CDNA4) as a validation target, the post demonstrates how Gluon-backed attention and MoE kernels achieve 1.6–3.6x end-to-end throughput improvements over portable Triton baselines, while NVIDIA paths (via FlashInfer/TensorRT-LLM wrappers) use the same public APIs. The AMD-specific kernels are published as a standalone pip package (tokenspeed-kernel-amd) reusable by other inference engines like vLLM.

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