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docs: fix formatting and wording in iluvatar and enflame device guides #295

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hami-robot merged 1 commit into from
May 6, 2026
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docs: fix formatting and wording in iluvatar and enflame device guides #295

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8 changes: 4 additions & 4 deletions docs/userguide/enflame-device/enable-enflame-gcu-sharing.md
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Expand Up @@ -7,13 +7,13 @@ title: Enable Enflame GPU Sharing

**HAMi now supports sharing on enflame.com/gcu(i.e., S60) by implementing most device-sharing features as nvidia-GPU**, including:

***GCU sharing***: Each task can allocate a portion of GCU instead of a whole GCU card, thus GCU can be shared among multiple tasks.
**GCU sharing**: Each task can allocate a portion of GCU instead of a whole GCU card, thus GCU can be shared among multiple tasks.

***Device Memory and Core Control***: GCUs can be allocated with certain percentage of device memory and core, we make sure that it does not exceed the boundary.
**Device Memory and Core Control**: GCUs can be allocated with a certain percentage of device memory and core, with hard limits enforced to prevent exceeding the allocation.

***Device UUID Selection***: You can specify which GCU devices to use or exclude using annotations.
**Device UUID Selection**: You can specify which GCU devices to use or exclude using annotations.

***Very Easy to use***: You don't need to modify your task yaml to use our scheduler. All your GPU jobs will be automatically supported after installation.
**No task YAML changes required**: All GCU jobs are automatically supported after installation.

## Prerequisites

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10 changes: 5 additions & 5 deletions docs/userguide/iluvatar-device/enable-iluvatar-gpu-sharing.md
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Expand Up @@ -6,15 +6,15 @@ title: Enable Iluvatar GPU Sharing

**HAMi now supports iluvatar.ai/gpu(i.e., MR-V100, BI-V150, BI-V100) by implementing most device-sharing features as nvidia-GPU**, including:

***GPU sharing***: Each task can allocate a portion of GPU instead of a whole GPU card, thus GPU can be shared among multiple tasks.
**GPU sharing**: Each task can allocate a portion of GPU instead of a whole GPU card, thus GPU can be shared among multiple tasks.

***Device Memory Control***: GPUs can be allocated with a specific device memory size, with hard limits enforced to prevent exceeding the allocation.
**Device Memory Control**: GPUs can be allocated with a specific device memory size, with hard limits enforced to prevent exceeding the allocation.

***Device Core Control***: GPUs can be allocated with limited compute cores, with hard limits enforced to prevent exceeding the allocation.
**Device Core Control**: GPUs can be allocated with limited compute cores, with hard limits enforced to prevent exceeding the allocation.

***Device UUID Selection***: You can specify which GPU devices to use or exclude using annotations.
**Device UUID Selection**: You can specify which GPU devices to use or exclude using annotations.

***Very Easy to use***: You don't need to modify your task yaml to use our scheduler. All your GPU jobs will be automatically supported after installation.
**No task YAML changes required**: All GPU jobs are automatically supported after installation.

## Prerequisites

Expand Down
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