> For the complete documentation index, see [llms.txt](https://documentation.alluxio.io/ee-ai-en/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.alluxio.io/ee-ai-en/release-notes.md).

# Release Notes

### Alluxio Enterprise AI 3.9

Alluxio AI 3.9 transforms Alluxio from a read-centric data cache into a full read/write data acceleration platform for AI, unlocking new use cases across the AI workflow while improving performance, reliability, and operational maturity.

This release advances two major product priorities:

* **Road to a Fast Read/Write Cache System** — Alluxio now supports write-intensive workloads with POSIX-compatible write cache and S3 multipart upload support, enabling model checkpointing and data preprocessing.
* **Strengthening the Data Engine for AI** — Native RDMA data transport, zero-copy and worker I/O optimizations, and stronger reliability improvements further strengthen Alluxio for demanding AI infrastructure.

#### New Features

**MLOps Workspace — FUSE Full POSIX Workspace**

{% hint style="warning" %}
Experimental since AI 3.9
{% endhint %}

Alluxio AI 3.9 introduces FUSE Full POSIX Workspace, enabling ML engineers to run interactive workloads directly on Alluxio-backed FUSE mounts with full POSIX semantics. Compared with basic FUSE write optimization, this mode supports random writes, overwrites, truncation, rename, symlinks, and other standard POSIX operations through a FUSE mount.

FDB-backed metadata enables multi-node access to the same dataset, while data can be stored on Worker NVMe for low latency or UFS PageStore for higher durability. Typical workloads include `git clone`, `vim`, `pip install`, continuous logging, data preprocessing, and migration of legacy POSIX applications without code changes. In validation testing, the Workspace reached up to **8.99 GB/s** peak sequential write throughput and **8.01 GB/s** peak hot-cache read throughput.

See [FUSE Full POSIX Workspace](/ee-ai-en/performance/fuse-workspace.md) for configuration and usage details.

**Model Training Checkpointing — S3 and FUSE**

{% hint style="warning" %}
Experimental since AI 3.9
{% endhint %}

Alluxio AI 3.9 adds high-performance checkpointing support for model training through both S3 and FUSE interfaces.

S3 write cache now supports standard Multipart Upload (MPU), enabling multi-gigabyte checkpoint files. In addition, checkpoint data is written to local cache first and then persisted asynchronously to object storage, reducing application-visible checkpoint latency and helping minimize GPU idle time during checkpoint operations.Validation testing showed up to **10.20 GB/s** single-worker checkpoint write throughput.

See [S3-API Write Optimization](/ee-ai-en/performance/s3-write-cache.md) and [FUSE Write Optimization](/ee-ai-en/performance/fuse-write-cache.md) for configuration and usage details.

**Native RDMA Data Transport**

{% hint style="warning" %}
Experimental since AI 3.9
{% endhint %}

Alluxio AI 3.9 adds native RDMA (Remote Direct Memory Access) transport for read I/O, bypassing the kernel networking stack to improve throughput and latency for data access workloads such as model loading, training data reads, and inference serving.

In single-node testing, RDMA reached **23.2 GB/s** on **200Gbps InfiniBand** and **49.5 GB/s** on **400Gbps InfiniBand**. In a 3-worker, 3-client cluster running on **200Gbps InfiniBand** nodes, RDMA scaled to **62.5 GB/s** aggregate throughput. Small-read latency reached **64 µs** P99 for 4 KB reads on 200G and approximately **59 µs** P99.9 on 400G.

RDMA support in this release applies to read I/O. Write cache write paths continue to use the standard TCP transport.

See [RDMA Networking](/ee-ai-en/performance/rdma-networking.md) for configuration and usage details.

#### Enhancements

**Cache Usage Insights from Access Logs**

Alluxio AI 3.9 introduces a cache observability framework that provides fine-grained access logs in addition to time-series metrics.

This helps with cache capacity planning, per-business-unit usage auditing, and chargeback analysis. The framework adds file-level visibility into hot and cold data distribution, per-workload access pattern analysis, and operational controls such as dynamic configuration, CLI-based log management, time-window deduplication, and sampling-rate tuning.

See [Access Log](/ee-ai-en/administration/audit-access-logs/access-log.md) for configuration and usage details.

**Cluster Operation Enhancements**

Alluxio AI 3.9 also improves cluster operations for large-scale deployments.

* **Recoverable data isolation for multi-tenant Kubernetes** — CSI-based subdirectory isolation replaces fragile `volumeMounts.subPath`, and mounted data survives FUSE pod restarts.
* **Independent worker service binding** — Worker RPC, REST, web, data, and RDMA services can use separate NIC and device bindings.
* **Job service reliability improvements** — Zombie job reconciliation, stable Job ID-based management, and stronger etcd-backed scheduler state handling improve operational robustness.
* **Write cache operational background tasks** — Async persist scanning, replica checks, orphan cleanup, invalid lock cleanup, and temp-file promotion are automated.
* **FUSE and deployment improvements** — Additional HDFS 3.4 compatibility, NAS UFS improvements, FUSE log rotation, and cluster diagnostics improve operability.