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Retrofit designs
Whether testing a new AI model or upgrading on-premises infrastructure, these ready-to-order retrofit designs streamline your next deployment.
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Quotable designs optimized for retrofits
| Total rack density | Total rack count | Total deployment size | Design ID | Cooling technology | ||
|---|---|---|---|---|---|---|
| NA | EMEA | ASIA | ||||
| 40kW | 4 | 160kW | 4X160R |
N/A | 4X160RA | Air |
| 70kW | 1 | 70kW | 1L88R |
1L88RE |
1L88RA |
Liquid + Air |
| 80kW | 1 | 80kW | 1X80 |
1X80E | 1X80A | Air |
| 100kW | 1 | 100kW | 1L100R |
1L100RE |
1L100RA |
Liquid + Air |
| 100kW | 4 | 400kW | 4L400R |
N/A | 4L400RA | Liquid + Air |
| 100kW | 4 | 400kW | 4XL400 |
4XL400E |
4XL400A |
Liquid + Air |
| 100kW | 5 | 500kW | 5L500 |
5L500E |
5L500A |
Liquid + Air |
| 100kW | 12 | 1200kW | 12XL1200 |
12XL1200E |
12XL1200A |
Liquid + Air |
| 100kW | 14 | 1400kW | 14L1400 |
14L1400E |
14L1400A |
Liquid + Air |
| 115kW | 1 | 132kW | N/A | 1XL115E |
N/A | Liquid + Air |
Reference designs
Vertiv's reference designs give you a clear starting point for AI data center builds and simplify integration for high-density workloads.
Design for new builds
| Total rack density | Total rack count | Total deployment size | Ref. GPU type | Design ID | Cooling technology | ||
|---|---|---|---|---|---|---|---|
| NA | EMEA | ASIA | |||||
| 20kW | 18 | 360kW | H100 | RD002 |
RD002E |
RD002A |
Air |
| 40kW | 10 | 360kW | H100 | RD003 |
RD003E |
RD003A |
Air |
| 130kW | 108 | 7MW | GB200 | RD020 |
RD020E | RD020A | Liquid + Air |
| 130kW | 18 | 1.2MW | GB200 | RD021 |
RD021E | RD021A | Liquid + Air |
| 58kW | 10 | 500kW | B200 | RD024 |
RD024E | RD024A | Air |
| 142kW | 40 | 2.5MW | GB300 | RD027 |
RD027E | RD027A | Liquid + Air |
| 142kW | 64 | 5MW | GB300 | RD028 |
RD028E | RD028A | Liquid + Air |
| 142kW | 80 | 5MW | GB300 | RD029 |
RD029E | RD029A | Liquid + Air |
| 142kW | 128 | 10MW | GB300 | RD030 |
RD030E | RD030A | Liquid + Air |
Vertiv design principles
Explore how implementing these principles can optimize the strategic deployment of AI workloads.
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