volcano-sh · safiyanasir2610 · Jun 16, 2026 · Jun 16, 2026
diff --git a/blog/1.4 release-en.md b/blog/1.4 release-en.md
@@ -10,7 +10,7 @@ tags: ["release", "volcano", "cncf"]
 
 
 Volcano, CNCF's first batch computing project, is now available with a new version, v1.4 (Beta). This version includes multiple important features, such as resource ratio-based partitions on GPU nodes, NUMA-aware, mixed deployment of multiple schedulers, and greatly improved stability.
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 __Resource ratio-based partitions on GPU nodes__ is developed to avoid idle GPUs while GPU-consuming jobs are starving. This is an important feature contributed by Leinao Cloud, a Volcano community member. 
 

diff --git a/blog/ING_case-en.md b/blog/ING_case-en.md
@@ -9,7 +9,7 @@ tags: ["case-study", "big-data", "analytics", "banking"]
 >On October 26, 2022, Krzysztof Adamski and Tinco Boekestijn from ING Group delivered a keynote speech "Efficient Scheduling Of High Performance Batch Computing For Analytics Workloads With Volcano" at KubeCon North America. The speech focused on how Volcano, a cloud native batch computing project, supports high-performance scheduling for big data analytics jobs on ING's data management platform.
 More details: [KubeCon + CloudNativeCon North America](https://events.linuxfoundation.org/archive/2022/kubecon-cloudnativecon-north-america/program/schedule/)
 
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 ## Introduction to ING
 
 Internationale Nederlanden Groep (ING), a global financial institution of Dutch origin, was created in 1991 with the merger of Dutch insurer Nationale-Nederlanden and national postal bank NMB Postbank.

diff --git a/blog/Meet Cloud Native Batch Computing with Volcano in AI & Big Data Scenarios.md b/blog/Meet Cloud Native Batch Computing with Volcano in AI & Big Data Scenarios.md
@@ -9,7 +9,7 @@ tags: ["kubecon", "batch-computing", "ai", "big-data", "volcano"]
 Cloud native batch computing engine Volcano is designed for high-performance computing applications such as AI, big data, gene sequencing, and rendering, and supports mainstream general computing frameworks. More than 58,000 global developers joined us, among whom the in-house ones come from companies such as Huawei, AWS, Baidu, Tencent, JD, and Xiaohongshu. There are 3.7k+ Stars and 800+ Forks for the project. Volcano has been proven feasible for mass data computing and analytics, such as AI, big data, and gene sequencing. Supported frameworks include Spark, Flink, TensorFlow, PyTorch, Argo, MindSpore, Paddlepaddle, Kubeflow, MPI, Horovod, MXNet, KubeGene, and Ray. The ecosystem is thriving with more developers and use cases coming up.
 ![](/img/blog/volcano_logo.svg) 
 
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 As the industry-first cloud native batch computing project，Volcano was Open-sourced at KubeCon Shanghai in June 2019, it became an official CNCF project in April 2020. In April 2022, Volcano was promoted to a CNCF incubating project. By now, more than 600 global developers have committed code to the project. The community is seeing growing popularity among developers, partners, and users.
 
 ### Try new features in Volcano v1.8.2

diff --git a/blog/Quick-Start-Volcano.md b/blog/Quick-Start-Volcano.md
@@ -29,7 +29,7 @@ volcanosh/vk-controllers   latest              7b11606ebfb8        10 seconds ag
 **NOTE**: Ensure that the images are correctly loaded to your Kubernetes cluster. For example, if you are using [kind luster](https://github.com/kubernetes-sigs/kind), run the ```kind load docker-image <image-name>:<tag> ``` command for each image.
 ### 2. Helm Charts
 
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 Install Helm charts.
 ```
 helm install installer/chart --namespace <namespace> --name <specified-name>

diff --git a/blog/Volcano-1.10.0-release.md b/blog/Volcano-1.10.0-release.md
@@ -7,7 +7,7 @@ tags: ["release", "volcano", "kubernetes", "scheduling"]
 ---
 On Sep 19, 2024, UTC+8, Volcano version v1.10.0 was officially released. This version introduced the following new features:
 
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 - **Support Queue Priority Scheduling Strategy**
 
 - **Enable Fine-Grained GPU Resource Sharing and Reclaim**
@@ -28,7 +28,7 @@ On Sep 19, 2024, UTC+8, Volcano version v1.10.0 was officially released. This ve
 
 - **Optimize Helm Chart Installation And Upgrade Processes**
 
-<!--{{<figure library="1" src="volcano_logo.png" width="50%">}}-->
+{/*{{<figure library="1" src="volcano_logo.png" width="50%">}}*/}
 Volcano is the industry-first cloud native batch computing project. Open-sourced at KubeCon Shanghai in June 2019, it became an official CNCF project in April 2020. In April 2022, Volcano was promoted to a CNCF incubating project. By now, more than 600 global developers have committed code to the project. The community is seeing growing popularity among developers, partners, and users.
 
 ## Key Features

diff --git a/blog/Volcano-1.11.0-release.md b/blog/Volcano-1.11.0-release.md
@@ -9,7 +9,7 @@ tags: ["release", "volcano", "kubernetes", "scheduling"]
 # Volcano v1.11 released: A New Era of Cloud-Native Scheduling for AI and Big Data
 
 As the de facto standard in cloud-native batch computing, Volcano has been widely adopted across various scenarios, including AI, Big Data, and High-Performance Computing (HPC). With over 800 contributors from more than 30 countries and tens of thousands of code commits, Volcano has been deployed in production environments by over 60 enterprises worldwide. It provides the industry with excellent practical standards and solutions for cloud native batch computing.
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 As user scenarios grow increasingly complex, especially in the scenarios of LLMs, there is a heightened demand for performance, GPU resource utilization, and availability in both training and inference workloads. This has driven Volcano to continuously expand its capabilities and address core user needs. Over the course of 28 releases, Volcano has introduced a series of enhancements and optimizations tailored to batch computing scenarios, helping users seamlessly migrate their workloads to cloud-native platforms. These improvements have resolved numerous pain points, earning the platform widespread praise and fostering a vibrant community with over 30 approvers and reviewers, creating a win-win ecosystem.
 
 The new release of Volcano will mark a new milestone in the New Year 2025, where the community will introduce a series of major features that will continue to deepen its focus on areas such as CNAI (Cloud Native AI) and Big Data, with key features including:

diff --git a/blog/Volcano-1.12.0-release.md b/blog/Volcano-1.12.0-release.md
@@ -10,7 +10,7 @@ tags: ["release", "volcano", "kubernetes", "scheduling"]
 
 As AI large model technology rapidly evolves, enterprises are placing higher demands on computing resource efficiency and application performance. For complex application scenarios such as AI, big data, and high-performance computing (HPC), efficiently utilizing accelerators like GPUs, ensuring high system availability, and managing resources with fine granularity are the core areas of focus for the Volcano community's continuous innovation.
 
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 Each version of Volcano is an active response to these challenges. With contributions from **over 1,000 developers from more than 30 countries, resulting in nearly 40,000 contributions**, Volcano has been adopted in production environments by more than 60 enterprises worldwide. Its scheduling performance and resource management capabilities have been widely proven in practice.
 

diff --git a/blog/Volcano-1.13.0-release.md b/blog/Volcano-1.13.0-release.md
@@ -9,7 +9,7 @@ tags: ["release", "volcano", "kubernetes", "llm", "scheduling"]
 # Volcano v1.13 Released: Comprehensive Enhancement of Scheduling Capabilities for LLM Training and Inference
 
 On September 29, 2025 (Beijing Time), [Volcano v1.13] (https://github.com/volcano-sh/volcano/releases/tag/v1.13.0)[1] was officially released. This update brings functional enhancements across multiple areas, providing users with a more comprehensive cloud-native batch computing solution.
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 ## Release Highlights
 

diff --git a/blog/Volcano-1.15.0-release-en.md b/blog/Volcano-1.15.0-release-en.md
@@ -21,7 +21,7 @@ As batch training, inference, AI Agent, HPC, big-data and other diverse workload
 
 The most notable new capability is **Gang-Aware Preemption and Resource Reclamation**: preemption decisions are evaluated at gang granularity on both the preemptor and victim sides — the preemptor is placed as a whole gang, and victim candidates are organized and evaluated at job/gang granularity, preferring surplus replicas to avoid per-Pod random eviction that disrupts multiple training jobs while the preemptor itself still cannot start. In addition, v1.15.0 introduces DRA queue quota in the capacity plugin, a pluggable multi-sharding policy framework, a Benchmark and performance observability tool, Kubernetes 1.35 support, NodeGroup preferred ordering, Agent Scheduler stability fixes, GPU/vGPU incremental enhancements, and Scheduling Gates for queue admission control.
 
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 ## Highlights
 

diff --git a/blog/Volcano-1.7.0-release-en.md b/blog/Volcano-1.7.0-release-en.md
@@ -20,7 +20,7 @@ Volcano 1.7.0 is now available with the following new features:
 ![volcano_logo.png](/img/blog/volcano_logo.svg)
 
 Volcano is the industry-first cloud native batch computing project. Open-sourced at KubeCon Shanghai in June 2019, it became an official CNCF project in April 2020. In April 2022, Volcano was promoted to a CNCF incubating project. By now, more than 490 global developers have committed code to the project. The community is seeing growing popularity among developers, partners, and users.
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 ### Key Features
 
 #### 1. Enhanced Plugin for PyTorch Jobs

diff --git a/blog/Volcano-1.8.2-release.md b/blog/Volcano-1.8.2-release.md
@@ -8,7 +8,7 @@ tags: ["release", "volcano", "kubernetes", "vgpu", "jobflow"]
 
 On January 9, 2024, UTC+8, Volcano version v1.8.2  was officially released. This version added the following new features:
 
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 - **Support for vGPU scheduling and isolation**
 
 - **Support for vGPU and user-defined resource preemption capabilities**

diff --git a/blog/Volcano-1.9.0-release.md b/blog/Volcano-1.9.0-release.md
@@ -8,7 +8,7 @@ tags: ["release", "volcano", "kubernetes", "scheduling", "gpu"]
 
 On May 21, 2024, UTC+8, Volcano version v1.9.0 was officially released. This version added the following new features:
 
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 - **Support elastic queue capacity scheduling**
 
 - **Supports affinity scheduling between queues and nodes**
@@ -23,7 +23,7 @@ On May 21, 2024, UTC+8, Volcano version v1.9.0 was officially released. This ver
 
 - **Improve scheduling stability**
 
-<!-- {{<figure library="1" src="volcano_logo.png" width="50%">}} -->
+{/* {{<figure library="1" src="volcano_logo.png" width="50%">}} */}
 Volcano is the industry-first cloud native batch computing project. Open-sourced at KubeCon Shanghai in June 2019, it became an official CNCF project in April 2020. In April 2022, Volcano was promoted to a CNCF incubating project. By now, more than 600 global developers have committed code to the project. The community is seeing growing popularity among developers, partners, and users.
 
 ### Key Features

diff --git a/blog/Volcano-community-co-construction-program.md b/blog/Volcano-community-co-construction-program.md
@@ -8,7 +8,7 @@ tags: ["community", "co-construction", "partners", "volcano", "huawei"]
 
 As artificial intelligence (AI) technologies advance and large language models (LLMs) grow more popular, the demand for AI compute has been booming. This has generated huge demand for high-performance scheduling for the AI and for hardware like AI chips.
 
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 ![](/img/blog/volcano_logo.svg)
 
 

diff --git a/blog/aiqiyi-en.md b/blog/aiqiyi-en.md
@@ -8,7 +8,7 @@ tags: ["case-study", "deep-learning", "migration", "kubernetes"]
 
 >This article was firstly released at `Container Cube` on September 30th, 2020, refer to[揭秘爱奇艺深度学习平台云原生迁移实践](https://mp.weixin.qq.com/s/YtP-ZURRBr5-ba1eWfKS2A)  
 
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 ## Introduction to iQIYI Jarvis Deep Learning Platform
 
 __Overall Architecture of the Platform__

diff --git a/blog/how-volcano-boosts-distributed-training-and-inference-performance.md b/blog/how-volcano-boosts-distributed-training-and-inference-performance.md
@@ -10,15 +10,15 @@ tags: ["performance", "distributed-training", "inference", "kubecon"]
 
 
 The increasing adoption of large language models (LLMs) has led to heightened demand for efficient AI training and inference workloads. As model size and complexity grow, distributed training and inference have become essential. However, this expansion introduces challenges in network communication, resource allocation, and fault recovery within large-scale distributed environments. These issues often create performance bottlenecks that hinder scalability.
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 ## Addressing Network Bottlenecks Through Topology-Aware Scheduling
 
 In LLM training, model parallelism distributes workloads across multiple nodes, requiring frequent data exchanges. Network communication can become a bottleneck, particularly in heterogeneous environments with InfiniBand (IB), RoCE, or NVSwitch configurations. Communication efficiency depends on network topology—fewer switches between nodes typically result in lower latency and higher throughput.
 One approach to mitigating this challenge is Network Topology-Aware Scheduling, which optimizes workload placement to minimize cross-switch communication. A key component of this strategy is the HyperNode, an abstraction for representing network topology via Custom Resource Definitions (CRDs). Unlike label-based methods, HyperNode provides a hierarchical structure that reflects actual network layouts, improving management and optimization. Nodes within the same HyperNode communicate more efficiently than those spanning multiple layers.
 
-<!-- TODO: Add network topology diagram when available -->
-<!-- ![](/img/blog/network-topology/hypernode-example.png) -->
+{/* TODO: Add network topology diagram when available */}
+{/* ![](/img/blog/network-topology/hypernode-example.png) */}
 
 Topology constraints can also be specified for jobs through the networkTopology field, with options for strict (Hard Mode) or flexible (Soft Mode) enforcement. This granular control helps ensure workloads are deployed in optimal network environments, reducing latency and improving throughput.
 
@@ -27,8 +27,8 @@ Topology constraints can also be specified for jobs through the networkTopology
 As AI workloads expand, single Kubernetes clusters may no longer suffice for large-scale training and inference. While multiple clusters can address this limitation, managing them efficiently presents challenges.
 The Volcano Global subproject extends scheduling capabilities to multi-cluster environments, integrating with Karmada to enable cross-cluster scheduling for distributed workloads. Features such as Queue Priority Scheduling, Job Priority Scheduling, and Multi-Tenant Fair Scheduling help optimize resource allocation and ensure equitable access across tenants. This approach simplifies multi-cluster management while supporting scalable AI workloads.
 
-<!-- TODO: Add multi-cluster architecture diagram when available -->
-<!-- ![](/img/blog/multi-cluster/volcano_global_design.svg) -->
+{/* TODO: Add multi-cluster architecture diagram when available */}
+{/* ![](/img/blog/multi-cluster/volcano_global_design.svg) */}
 
 ## Improving Stability with Fine-Grained Fault Recovery
 

diff --git a/blog/hpc-en.md b/blog/hpc-en.md
@@ -12,7 +12,7 @@ tags: ["hpc", "case-study", "meteorological", "wrf"]
 
 
 Kubernetes has become the de facto standard for cloud native application orchestration and management. An increasing number of applications are being reconstructed or built to employ Kubernetes. High performance computing (HPC) is a popular distributed computing mode and is widely used in many fields. For users who have deployed HPC applications and are eager to containerize and manage their applications using Kubernetes, Volcano, CNCF's first distributed scheduling system for batch computing, is a good choice. Volcano supports multiple types of computing frameworks, such as Spark, TensorFlow, and Message Passing Interface (MPI). This article uses a traditional HPC application, the Weather Research and Forecasting (WRF) model, as an example to describe how Volcano works for HPC applications.
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 ## About HPC

diff --git a/blog/iflytek_case_study.md b/blog/iflytek_case_study.md
@@ -10,7 +10,7 @@ tags: ["case-study", "iflytek", "ai", "infrastructure", "award"]
 
 
 [HONG KONG, CHINA — June 10, 2025] — The Cloud Native Computing Foundation (CNCF) today announced that iFlytek has won the CNCF End-User Case Study Competition. The CNCF, which is committed to building a sustainable ecosystem for cloud native software, recognized iFlytek for its innovative use of Volcano. The company shared its success in large-scale AI model training at the KubeCon + CloudNativeCon China conference, held in Hong Kong from June 10-11.
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 ### iFlytek's Challenges
 

diff --git a/blog/introducing-kthena-redefining-llm-inference-for-the-cloud-native-era.md b/blog/introducing-kthena-redefining-llm-inference-for-the-cloud-native-era.md
@@ -11,7 +11,7 @@ Today, the [Volcano](https://volcano.sh/) community is proud to announce the lau
 Kthena is a cloud-native, high-performance system for LLM inference routing, orchestration, and scheduling, tailored specifically for Kubernetes. Engineered to address the complexity of serving LLMs at production scale, Kthena delivers granular control and enhanced flexibility. Through features like topology-aware scheduling, KV Cache-aware routing, and Prefill-Decode (PD) disaggregation, it significantly improves GPU/NPU utilization and throughput while minimizing latency.
 
 As a sub-project of Volcano, Kthena extends Volcano’s capabilities beyond AI training, creating a unified, end-to-end solution for the entire AI lifecycle.
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 ## The "Last Mile" Challenge of LLM Serving
 

diff --git a/blog/kube-batch-customers.md b/blog/kube-batch-customers.md
@@ -15,4 +15,4 @@ tags: ["kube-batch", "customers", "kubernetes", "scheduler"]
 | [FfDL](https://github.com/IBM/FfDL)  | [@animeshsingh](https://github.com/animeshsingh)| |  |
 | [MOGU Inc](https://www.mogujie.com)  | [@jiaxuanzhou](https://github.com/jiaxuanzhou)| Production | The scheduler for offline training of tiny+ |
 
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diff --git a/blog/kube-batch-startup.md b/blog/kube-batch-startup.md
@@ -10,7 +10,7 @@ tags: ["kube-batch", "startup", "kubernetes", "scheduler"]
 
 
 This document describes how to run `kube-batch` as a batch scheduler for Kubernetes. To get the complete code, go to [master](https://github.com/kubernetes-sigs/kube-batch/tree/master).
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 ## 1. Prerequisites
 Before running `kube-batch`, you must start up a Kubernetes cluster (see [Creating a Cluster with Kubeadm](https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/)). To complete local testing and deployment, you can use Minikube (see [Running Kubernetes Locally with Minikube](https://kubernetes.io/docs/getting-started-guides/minikube/). You can also use [kind](https://github.com/kubernetes-sigs/kind) to run local Kubernetes clusters with Docker container "nodes".

diff --git a/blog/leinao-en.md b/blog/leinao-en.md
@@ -20,7 +20,7 @@ The Leinao cloud AI platform includes an AI development platform, public service
 - The AI visualized operations platform helps managers make better informed decisions. 
 
 - The AI community is where AI developers and companies gather, exchange ideas, and improve their skills.
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 ## The architecture of Leinao cloud OS
@@ -47,15 +47,15 @@ More importantly, Volcano allows you to configure retry policies for distributed
 
 Volcano provides enhanced job APIs.  
 
-<!--![](leinao-en2.png)-->  
+{/*![](leinao-en2.png)*/}  
 
 Volcano improves many aspects of default-scheduler.
 
-<!--![](leinao-en3.png)--> 
+{/*![](leinao-en3.png)*/} 
 
 Default-scheduler and Volcano work differently in a couple of other ways as well.
 
-<!--![](leinao-en4.png)--> 
+{/*![](leinao-en4.png)*/} 
 
 
 

diff --git a/blog/paddlepaddle-en.md b/blog/paddlepaddle-en.md
@@ -11,7 +11,7 @@ tags: ["paddlepaddle", "distributed-training", "ai", "framework"]
 
 PaddlePaddle is a deep learning framework open-sourced by Baidu in September 2016. It aims to provide a secure, easy-to-use, and scalable deep learning platform.
 
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diff --git a/blog/pengcheng-en.md b/blog/pengcheng-en.md
@@ -8,7 +8,7 @@ tags: ["case-study", "scientific-research", "preemption", "kubernetes"]
 
 >This article was firstly released at `Container Cube` on September 30th, 2020, refer to[鹏城实验室启智章鱼教你彻底摆脱Kubernetes集群资源抢占难题](https://mp.weixin.qq.com/s/h4T7KbAiQZTKepYcTcgdlA)  
 
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 ## Introduction to OpenI-Octopus
 
 OpenI-Octopus is a cluster management and resource scheduling system developed and maintained by Peng Cheng Laboratory, Peking University, and University of Science and Technology of China.
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		PaddlePaddle is a deep learning framework open-sourced by Baidu in September 2016. It aims to provide a secure, easy-to-use, and scalable deep learning platform.

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