Kubernetes is the best way to run AI workloads. Inference with Large Language Models (LLMs), training your own models, and all of the support infrastructure required to create reliable AI applications have benefited from years of scalability and the ecosystem that Kubernetes has provided.
Many companies only know Kubernetes as a hyperscaler cloud service they rent.
They build AI on top of what their cloud provider gives them access to, at the timeline the cloud provider requires, and at the rate their budgets can sustain.
Self-hosted AI inference is no longer a novel option; it's a necessity for a lot of organizations. Data-residency requirements and requirements to reduce token spend, without impacting productivity, are moving AI on-prem — or at least onto infrastructure the company owns. Thankfully, Talos Linux is a great way to run AI workloads on Kubernetes wherever you want.
CapEx vs OpEx
Moving fast in business requires you try something before you buy it. If you're testing the agentic software development waters, you shouldn't be spending hundreds of thousands of dollars up front. You need to validate that the technology works in your environment before you can accurately scope the return on investment and shift from a rental to an ownership mindset.
Owning your infrastructure gives you predictable costs and stable platforms to build on. You're not beholden to companies or governments removing access to models based on their motivations. A one-time capital investment can provide all of the tokens your company needs, but building the required infrastructure comes with a steep learning curve.
If your consumption is less than your production capacity, you have an infinite supply. No recurring rental fees, no risk of models being removed, and no price hikes. You can own the infrastructure and Talos Linux can help you secure and maintain it long term.
Now is the perfect time to switch from a subscription-based model to owning your critical AI infrastructure. Open-weight AI models have become increasingly capable in 2026, and they are at or above the level most developers need. The Pragmatic Engineer reports that Uber, Pinterest, Stripe, Coinbase, Ramp, and AT&T have all moved simpler workloads off proprietary models, cutting their AI bills by roughly half. Newer, better models will likely come from foundation AI companies, but using them will be like driving a Ferrari to the grocery store.
Why Talos Linux
Talos Linux was one of the first CNCF AI-conformant distributions for Kubernetes. It is designed to reduce maintenance at any scale with repeatable, declarative configuration. This applies to the base OS configuration, Kubernetes, and AI accelerators. Adding NVIDIA drivers is as simple as adding a system extension. The system extension is already tested and validated with that version of Talos Linux, and it will keep working with each version upgrade.
Setting up one machine with a traditional Linux distribution might be easy to do, but upgrades and scaling to dozens or hundreds of machines over years of usage have always been a problem. You have to know the exact features of your OS packages and Linux kernel, plus hardware compatibility and driver requirements. Talos Linux eliminates this work by shipping Long Term Support (LTS) Linux kernels and LTS and Production branches of NVIDIA drivers with each release. We also ship drivers for AMD, Intel, Tenstorrent, and Hailo AI accelerators.
Once you try to scale AI workloads to multiple machines, different accelerators, or multiple models, you're going to wish you had a single API that could do it all.
That's exactly what Talos Linux provides: an API for Linux that runs in any environment.
Installation and provisioning with Talos Omni
Talos Linux is already easier to install than a general-purpose Linux distro.
Talos Omni turns "easier" into "boring" because it encompasses more components needed by enterprises. Authentication and role-based access control (RBAC), machine discovery, and system extensions are managed automatically. Talos Linux and Kubernetes upgrades are a single button push or API call.
Clusters are provisioned declaratively in Omni. Kubernetes version, Talos Linux version, NVIDIA drivers, and any system customizations all live in one declarative template. Here is a full, working example with a single control plane node and two GPU workers:
kind: Cluster
name: inference
kubernetes:
version: v1.37.1
manifests:
- name: gpu-operator
mode: full
file: manifests/gpu-operator.yaml
- name: resource-claim-templates
mode: full
file: manifests/resource-claim-templates.yaml
- name: deepseek-vllm
mode: full
file: manifests/vllm.yaml
talos:
version: v1.14.0
---
kind: ControlPlane
machines:
- 00000000-0000-0000-0000-00000000000a
---
kind: Workers
machines:
- 00000000-0000-0000-0000-00000000000b
- 00000000-0000-0000-0000-00000000000c
---
kind: Machine
name: 00000000-0000-0000-0000-00000000000b
systemExtensions:
- siderolabs/nonfree-kmod-nvidia-production
- siderolabs/nvidia-container-toolkit-production
patches:
- file: patches/nvidia-modules.yaml
---
kind: Machine
name: 00000000-0000-0000-0000-00000000000c
systemExtensions:
- siderolabs/nonfree-kmod-nvidia-production
- siderolabs/nvidia-container-toolkit-production
patches:
- file: patches/nvidia-modules.yamlThe template includes manifests and patches that live next to `cluster.yaml`. If you want to see the full example, check out the contrib repository — you can base your own deployment on the examples there.
Once you have the cluster template defined, you can go from bare-metal hardware to LLM endpoint with a single command.
omnictl cluster template sync --file cluster.yamlEvery change to maintain the cluster — bumping the Kubernetes version, adding another worker, or deploying a different model — is a simple edit to the template and another `sync`. Omni handles the extension rebuilds, staged reboots, manifest re-application, and re-joins on its own.
Private AI with the Talos Platform
Self-hosted AI is not a simple problem, and it's much more than buying hardware and putting it in a data center. You might be able to stand up a proof-of-concept environment with a guide, but scaling that to be reliable for enterprise use needs more than automation; it needs repeatable, declarative consistency.
With Talos Linux, everything from the kernel up to the workload is declarative and immutable. NVIDIA drivers, container toolkits, and the LTS kernel are validated and signed at build time. They contain Software Bills of Materials (SBOMs) to verify what software is running in your infrastructure, and there's no post-install `apt-get` step where a supply-chain compromise or a drifting configuration can sneak in.
The same declarative template that stood up two GPU workers can stand up two hundred, and because every node comes from the same image with the same extensions and the same patches, "works on this cluster" means "works on every cluster." Omni bounds the blast radius of any change: a driver bump, a Kubernetes upgrade, a new Dynamic Resource Allocation (DRA) claim, or a new model deployment all move through the same reconcile loop with staged reboots and health gates. Compliance and data-residency requirements become properties of the template you commit to Git, not tribal knowledge locked in someone's head.
If you want to try this on your own hardware, Omni has a free 30-day trial.
Amarachi Iheanacho
Hannah Augur