Start with the application you want to run and the tools your team knows. A distribution's name does not tell you how many tokens per second a GPU will generate. The complete stack matters: driver, libraries, inference engine and configuration.
Ubuntu, Debian and Rocky Linux can all form the basis of an LLM server. This guide compares how to prepare and maintain an environment, rather than presenting a performance contest between the three systems.
First, choose how to run the model
There are three practical approaches:
- A prebuilt application with libraries. Choose a specific build, check its requirements and run it with a compatible driver. Our private llama.cpp API guide uses this approach.
- A container. Package the application's user-space dependencies with it. The host still needs a working driver and correctly configured GPU access.
- A source build. Also choose a CUDA Toolkit, compiler and development libraries. This gives control over the build, with more components to record and reproduce.
This distinction is often more useful than “Ubuntu or Debian”. Running a prebuilt CUDA application does not automatically require installing the entire development toolkit.
When to consider each distribution
| System | A practical starting point | What to check before installing |
|---|---|---|
| Ubuntu LTS | Your team already uses Ubuntu, or your application documents that environment | the exact LTS release, application requirements and a compatible driver installation path |
| Debian stable | You want to keep the administration approach used on your other Debian servers | the Debian release supported by the chosen NVIDIA stack, not just the word “stable” |
| Rocky Linux | You already use RPM/DNF tools and Enterprise Linux procedures | the exact Rocky release, kernel, driver and application libraries |
These are operational criteria, not performance rankings. Ubuntu documents its LTS release cycle, Debian recommends stable for production, and Rocky documents RPM/DNF package management. A maintained environment your team understands is a more useful starting point than changing operating systems for the name alone.
Driver, runtime and Toolkit are different components
The driver connects the operating system to the card. Runtime libraries are used by the application, while the Toolkit also includes development tools. Record each component's version separately.
NVIDIA publishes a distribution support matrix for each CUDA release. Do not transfer compatibility claims automatically between releases. For example, the CUDA 12.8.1 guide lists specific Ubuntu, Debian and Rocky 8/9 releases; it does not list Rocky 10. A newer matrix can have a different scope.
Our published llama.cpp tests ran on Rocky Linux 10.2 with NVIDIA driver 595.91.07 and CUDA 12.8 libraries supplied alongside the pinned application build. That verifies this combination in the described runs. When building your own application, select the full Toolkit and compiler using the relevant documentation.
The “CUDA Version” field in nvidia-smi describes driver capabilities, not a complete inventory of installed libraries. When changing versions, consult the CUDA and driver compatibility rules.
Does a container solve compatibility?
A container helps keep the application and its dependencies consistent. It does not replace the host driver or correct GPU access. The pinned llama.cpp container documentation describes a CUDA variant, and NVIDIA lists Container Toolkit platform support.
To reproduce an environment, record an exact image version or digest, the model and its checksum, and the launch settings. A moving latest tag is convenient for experiments, but does not identify the image used in an earlier measurement.
How to check your choice
Before moving an application, run a short check: load the same model, verify the intended GPU, send a representative request and record time to first token and response throughput. Then check service restart and model availability on local storage.
For a distribution performance comparison, keep the hardware, model, application build, driver, context and concurrency consistent. Changing all of them together measures the difference between two environments, not the effect of the operating system alone.
The practical choice: select a distribution compatible with your application and workflow, then pin the working environment. Judge speed using LLM engine measurements and API response tests, not the operating system's popularity.
View the server configuration or tell us which model and environment you want to run.