Fusion Rack AI 200
2 x 48GB pooled, 64 cores, 384GB, 4U of rack
Two 48GB cards in a rack, shared by a team.
- Processor
- AMD 64 Core Processor
- Graphics
- HPE 96 GB Vram graphics card
- Memory
- 384GB High speed memory
- Form
- 4U rack
AI & Machine Learning
AI and GPU servers
Video memory decides which models fit.
Our machines
AI and GPU servers we build
Fusion Rack AI 300
4 x 48GB pooled, 96 cores, 768GB, 100GbE
192GB of card memory in one node, for the largest models.
- Processor
- AMD 96 Core Processor
- Graphics
- HPE 192 GB Vram graphics card
- Memory
- 768GB High speed memory
- Form
- 4U rack
Side by side
Compare them
| Specification | ||
|---|---|---|
| Best for | Two 48GB cards in a rack, shared by a team. | 192GB of card memory in one node, for the largest models. |
| Processor | AMD 64 Core Processor | AMD 96 Core Processor |
| Graphics | HPE 96 GB Vram graphics card | HPE 192 GB Vram graphics card |
| Memory | 384GB High speed memory | 768GB High speed memory |
| Storage | 17.28TB total | 65.28TB total |
| Form | 4U rack | 4U rack |
| Actions |
Before you choose
What AI & Machine Learning work needs
1
Video memory capacity, ahead of everything
A model larger than the card will not start, however fast the card is.
2
Memory bandwidth on the card
Once the model fits, memory bandwidth sets how fast it answers.
3
A host that can keep the loader ahead of the card
Enough cores and fast drives that the card never waits for data.
By software
Sized for the programs you run
Not sure which one?
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