Alibaba Cloud vs Red Hat

Alibaba's 20GW Data Center Target Rests on T-Head Chips

At its Apsara conference, Alibaba CEO Eddie Wu set an ambitious target for Alibaba Cloud to operate over 20 gigawatts (GW) of data center capacity worldwide by 2032. The announcement was made on September 22, 2026, and is seen as a major infrastructure bet. Analysts at Goldman Sachs, Citi, and UBS have provided financial forecasts, estimating significant capital expenditures and external cloud revenue growth. Goldman Sachs highlights that T-Head, Alibaba's in-house chip unit, is crucial to the economics of the target, with its share of computing power expected to rise from about 10% to half over the medium term. The article notes that Alibaba has not disclosed a spending figure for this target, and that the plan relies heavily on T-Head chips, whose supply was previously noted as limited. The 20GW target represents a significant scale-up from Alibaba's current estimated capacity of 5-6GW.

• Alibaba Cloud aims to operate more than 20GW of data center capacity worldwide by 2032.
• CEO Eddie Wu announced the target at the Apsara conference on September 22, 2026.
• Goldman Sachs expects T-Head's share of Alibaba Cloud's computing power to rise from about 10% to about half over the medium term.

Alibaba unveils Zhenwu V900 AI chip, expands data centers

Alibaba shares rose about 3% in Hong Kong following the Apsara Conference 2026, where CEO Eddie Wu unveiled a full-stack AI strategy integrating proprietary AI models, custom chips, and cloud infrastructure. The company introduced the Zhenwu V900 AI chip, with three times the performance of its predecessor and clusters scaling to 500,000 accelerators, slated for mass production in Q1 2027. Plans include expanding Alibaba Cloud's global data center capacity to over 20 gigawatts by 2032. Alibaba also revealed its Qwen model family expansion, including a new model with 5-10 trillion parameters and the Qwen Intelligence for on-device AI on smartphones and PCs. These moves mirror similar investments by Nvidia, Meta, and Huawei, positioning Alibaba as an infrastructure powerhouse in the global AI race.

• Alibaba unveiled the Zhenwu V900 AI chip, offering three times the performance of its predecessor, with mass production set for Q1 2027.
• Alibaba plans to expand Alibaba Cloud's data center capacity to over 20 gigawatts by 2032.
• Alibaba announced a new AI model with 5-10 trillion parameters as part of its Qwen model expansion.

DeepSeek-V4.1-Flash Is Now on Alibaba Cloud Token Plan: How to Get Started from $6 a Month

DeepSeek-V4.1-Flash is now available on Alibaba Cloud Token Plan, starting from $6 a month. This is a new model addition to the platform.

CISA flags three actively exploited Linux kernel flaws

The US Cybersecurity and Infrastructure Security Agency (CISA) has added three Linux kernel vulnerabilities to its Known Exploited Vulnerabilities Catalog (KEV), indicating they are being actively exploited. The flaws, tracked as CVE-2025-39682, CVE-2026-53266, and CVE-2025-39964, are rated as 'critical' or 'high' severity. Red Hat has confirmed exploitation via publicly known exploits. The vulnerabilities can lead to system crashes, privilege escalation, and remote code execution. CISA has ordered US federal agencies to patch affected systems within three days or temporarily take them offline. Patches are available in the kernel, and administrators are urged to apply them urgently. No details on the threat actors or targets have been disclosed yet.

• CISA added three Linux kernel vulnerabilities to its KEV catalog: CVE-2025-39682, CVE-2026-53266, and CVE-2025-39964.
• Red Hat confirmed that all three vulnerabilities are exploited in real attacks via publicly known exploits.
• CVE-2025-39682 involves an error in processing empty TLS records in kernel TLS, potentially leading to system crashes and code injection.

Tokens-per-Second Benchmarks Explained

This technical guide explains what "tokens per second" (tok/s) actually measures for local LLM inference, why single-user tok/s numbers can be misleading, and how concurrency, batching, and prompt processing change the observed speed. It contrasts single-user latency with server throughput, highlights vLLM's continuous-batching advantage versus Ollama under high concurrency, defines related metrics (P99 latency, time to first token / TTFT), and provides practical measurement advice using tools like Ollama and vLLM and calculators from notAcalculator. The article also gives realistic tok/s expectations for different model sizes on consumer hardware and lists practical tips for reading and running benchmarks yourself.

• Tokens are the unit of both billing and speed for LLMs; tokenization affects cost and measured tok/s.
• Under a Red Hat benchmark on an A100 40GB with Llama 3.1 8B, vLLM peaked around 793 tok/s combined throughput versus about 41 tok/s for Ollama at high concurrency (~19x gap).
• vLLM's key innovation is continuous batching (plus PagedAttention), which increases total throughput under concurrency compared with single-request processing tools.

Spring Boot IAM: OAuth2 Redirect Bug in Production

The author built identityCore, a self-hosted Identity & Access Management (IAM) service in Spring Boot, implementing form login plus Google (OIDC) and GitHub (OAuth2) logins, RBAC stored as JPA entities, and a unified provisioning flow. The post explains key differences between OAuth2 and OIDC (GitHub returns an opaque access_token requiring extra API calls; Google returns an id_token JWT), and describes a production-only bug where OAuth2 logins failed with redirect_uri_mismatch because TLS was terminated upstream and the app ignored X-Forwarded headers. The one-line fix was to set server.forward-headers-strategy=framework so Spring trusts proxy headers. The author lists operational lessons about protocol differences, deployment vs demo differences, and centralized user provisioning.

• identityCore is a self-hosted IAM service built with Spring Boot (stack: Spring Boot 3.3.5, Spring Security 6.3.4, Spring Data JPA, PostgreSQL/H2, Thymeleaf, HikariCP, BCrypt).
• The system supports three login paths (form login, Google via OIDC, GitHub via OAuth2) that resolve to a single UserEntity and use JPA RoleEntity / PermissionEntity for RBAC.
• GitHub returns an opaque access_token requiring downstream calls (e.g., GET /user and /user/emails) to obtain a verified email; Google returns an id_token (JWT) containing email and email_verified claims.