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- clarify memory table states before model load
- call out desktop and app footprint avoided by minimized Server
- align decision bullet with measured desktop footprint
Copy file name to clipboardExpand all lines: _posts/2026-06-22-ubuntu-gb10.md
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@@ -44,13 +44,14 @@ DGX OS is not bad. For a first-time GX10 owner, it is the lowest-friction path t
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The reasons I moved away from it:
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**Memory footprint.** DGX OS ships with a full GNOME desktop. On a machine where the CPU and GPU share the same physical memory, that desktop costs you model capacity. Measured on identical GX10 units:
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**Memory footprint.** DGX OS ships with a full GNOME desktop. On a machine where the CPU and GPU share the same physical memory, that desktop costs you model capacity. Measured on identical GX10 units before loading a model, with the final row showing the practical desktop footprint avoided by the minimized Server path:
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|Configuration | RAM used at idle| Available for models |
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|System state | Memory impact before model load| Available for models |
|**Ubuntu Server 24.04 minimized (this guide)**|**~2.9 GiB**|**~118 GiB**|
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| DGX OS 7 with GNOME idle |~4.7 GiB |~116 GiB |
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| DGX OS 7 used as a desktop, with normal apps open |~5.8 GiB |~115 GiB |
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|**Ubuntu Server 24.04 minimized, with no desktop session**|**~2.9 GiB**|**~118 GiB**|
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|**Desktop/app footprint avoided by the minimized Server path**|**Up to ~5.8 GiB of GNOME and desktop-app usage is not present**|**~118 GiB available after the minimized Server base OS is running**|
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Xorg plus gnome-shell plus gnome-remote-desktop pulls about 340 MB of the shared GPU memory on its own, before you start doing any AI work.
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@@ -93,7 +94,7 @@ If you just bought a GX10 or DGX Spark and want the path of least resistance, DG
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I would use this path when you want:
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- To reclaim roughly 3-6 GiB of RAM otherwise spent on the desktop stack, based on the GNOME idle and apps-open measurements above
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- To avoid up to ~5.8 GiB of GNOME and desktop-app memory footprint before your model starts
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- No desktop environment consuming GPU memory on a machine built to run LLMs
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- Fewer installed packages, which means less patching, less bloat, and less attack surface
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- Control over update cadence - driver and kernel upgrades on your schedule
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