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Putting Swift's S3 API to Work for AI Workloads on OpenStack Flex

I'll be honest. When someone says "object storage for AI" the picture in most people's heads is a purpose-built S3 appliance with a six-figure invoice attached, or a hyperscaler bucket with an egress meter running. Swift, the OpenStack object store that has been quietly holding petabytes since 2010, rarely makes the list. It's old. It doesn't have a launch video. It speaks S3 through a middleware rather than natively.

Our team has been leaning on that middleware harder every month. Model weights, training shards, and lately the KV cache that vLLM spills out of GPU memory all land in Swift buckets through the S3 API, and at some point "it seems fine" stops being an acceptable answer to "how fast is it." So we did what you're supposed to do. We picked an open source benchmark, built a container around it, ran the object sizes that AI workloads actually produce, and kept the numbers.

Rescuing a Linux based Boot-From-Volume Instance in OpenStack

When an OpenStack instance backed by a persistent Cinder volume (Boot-From-Volume, or BFV) gets corrupted, misconfigured, or suffers a broken bootloader, recovery can be challenging. However, OpenStack control planes (introduced in Ussuri) support Stable Device Instance Rescue for volume-backed instances.

This guide provides a comprehensive guide to help analyze the storage layout and some basic commands to reapir a filesystem using nativ Linux tools.

Bringing the AMD Radeon AI PRO R9700 Online in OpenStack Flex

I'll be honest. When the AMD Radeon AI PRO R9700 first showed up on my radar, I wasn't sure what to make of it. It's not a traditional datacenter card and it's not a gaming card either. The R9700 is a 32 GB professional GPU that won't break the bank, and sits in a product category that didn't really exist eighteen months ago.

This week our team brought a pair of R9700 GPUs online in Rackspace OpenStack Flex; like any good story there was a bit of drama with servers, placement, shipping times, cables oddities, chassis crisis, and more; we had the making of a full feature length K-Drama with all the twists and turns. Once we got past the drama, parts were installed and powered on, the entire deployment took about ten minutes which is a testament to the power of Genestack's Kubernetes-native architecture and OpenStack's hardware-agnostic design.

Getting Started with AMD GPU Compute on Rackspace OpenStack Flex

Your instance is up, your AMD GPU is attached, and you're staring at a terminal with no nvidia-smi to lean on. Welcome to the other side.

If you've read our NVIDIA getting started guide, you know the drill: provision an instance, install drivers, verify the hardware, start computing. The AMD path follows the same logic but with different tooling. Instead of CUDA, you're working with ROCm. Instead of nvidia-smi, you've got rocm-smi. Instead of a driver ecosystem that's had two decades of cloud deployment polish, you've got one that's been moving fast and getting dramatically better, but still has some rough edges worth knowing about.

The Business Case for CPU-Based AI Inference

Your finance team doesn't care about tokens per second. They care about predictable costs, compliance risk, and vendor lock-in. Here's how CPU inference stacks up.

The other week I published a technical deep-dive on running LLM inference with AMD EPYC processors and ZenDNN. The benchmarks showed that a $0.79/hour VM can push 40-125 tokens per second depending on model size, genuinely usable performance for a surprising range of workloads.

But benchmarks don't answer the question that actually matters: Should you do this?