# 计算 · 日跟踪 · 2026-10-22

> 日期：2026-10-22（Asia/Shanghai，周四）。窗口：2026-10-21（周三）早报之后 → 2026-10-22（周四）早。上一成功早报 **2026-10-21**。抓取全库：Saved 268 / skipped 28 / errors 46；blocked_hosts：community.hpe.com、docs.redhat.com、e.huawei.com、support.huawei.com、www.globenewswire.com、www.reddit.com、www.sangfor.com（与 10-21 相同）。相对全部历史归档真正新增 URL 15 条（industry 6、redhat 4、nutanix 3、smartx 1、zstack 1）。

## 摘要
- **中 NEW · Vera CPU 成 agent 执行侧算力：** Nutanix 计划 Day Zero 支持（NKP 裸金属→AHV），立论「agent 工具执行/沙箱跑在 CPU」；NVIDIA OpenShell RA 同样让 Vera CPU 跑 agent——**厂商/媒体**。
- **中 NEW · RHEL for NVIDIA 26.03**（仅改 kernel/QEMU/libvirt 首发支持新硬件，可 off-ramp 回主线）；**NC2 on GCP Z4D**（5 代 EPYC 裸金属）。

## 原文摘录
### 1
- 归档：`archive/2026-10-22/nutanix/the-cpu-engine-of-agentic-ai-why-nutanix-and-nvidia-vera-are-redefinin-b019799b.txt`
- URL：https://www.nutanix.com/blog/the-cpu-engine-of-agentic-ai
- 口径：厂商博

> All of these activities run on CPUs, making CPU performance an increasingly important part of the AI infrastructure equation. Early generative AI deployments focused primarily on GPU capacity. Organizations evaluated how quickly they could train or serve models, how much GPU memory they needed, and how to optimize inference. Agentic AI introduces a broader set of requirements. An agent may need to: - Execute code in…

### 2
- 归档：`archive/2026-10-22/redhat/rhel-for-nvidia-delivers-trusted-foundation-for-next-gen-ai-workloads-8d022e61.txt`
- URL：https://www.redhat.com/en/blog/rhel-nvidia-delivers-trusted-foundation-next-gen-ai-workloads
- 口径：厂商博

> modifying a strictly limited set of core packages—the Linux kernel, QEMU, and libvirt—to enable launch-day hardware support. The rest of the userspace matches standard RHEL. Once those hardware enablement patches mature and merge into the upstream Linux kernel, organizations can seamlessly transition back to mainline RHEL via an off-ramp without reinstalling. That is the fast lane. It is not a separate product, and…