

Compact 2/3-size design
Expandable with up to 3 additional GPUS
Based on comparison with other GPU servers
PERFORMANCE
Up to
7Number of AI processors
that can be installed
Up to
27.7PFLOPSAI performance
(Based on 7 H100 NVL, FP8)
Up to
900GB/sGPU-to-GPU communication speed
(With H200 NVL NVLink bridge)
Up to
846GBHBM3E GPU memory
Up to
1,821BParameter model capacity
About 10x the size of GPT-3
Up to
2.29xPerformance improvement
Compared to dg4W maximum performance
RACK MOUNT

deep gadget isn't just another liquid cooling solution. It's a next-generation system, precision-engineered
with expertise from Seoul National University's supercomputer research, designed by a 20-year Toyota Motor Corporation veteran, and refined through a decade of R&D.
Management-free built-in Liquid Cooling System
A fully sealed inner liquid conduit ensures the world's highest level of stability.

dg5W
Cooling System
General custom
Cooling System
Equipped with world-class Dual-Pump cooling systems to prevent heat-induced GPU throttling and extend life
deep gadget
GPU performance does not deteriorate as the temperature remains around 60℃, even if the GPU is used at its maximum.General GPU server
The GPU operating clock automatically drops and performance decreases as the GPU temperature rises to 80~90℃.World-leading adaptive Liquid Cooling System
deep gadget adaptively controls the Liquid Cooling System by constantly checking the system's usage rate and temperature.CUTTING-EDGE ARCHITECTURE

Support for various GPUs
In AI learning, performance of TF32 and BFLOAT16 is crucial. GPUs that can be installed in deep gadgets have the following rankings for the corresponding metrics. With a robust product line and comprehensive GPU support, professionals requiring high specifications are poised for success.| Model | Manufacturer | Memory | AI learning Perf TF32(FP32) (TFLOPS) | AI Perf BF16(FP16) (TFLOPS) | AI interfernce Perf INT8|FP8|FP4 (TOPS/TFLOPS) | Precision computation Perf FP64 (TFLOPS) | General purpose computing Perf FP32 (TFLOPS) | Rendering Perf RT Core (TFLOPS) |
|---|---|---|---|---|---|---|---|---|
| RTX PRO 6000 Blackwell Server Edition | NVIDIA | GDDR7 96GB | 234 (468*) | 468 (936*) | 1,892 (3,784*) | 1.88 | 120 | 354.5 |
| RTX PRO 6000 Blackwell Max-Q | NVIDIA | GDDR7 96GB | 219 (438*) | 438 (876*) | 1,755 (3,511*) | 1.72 | 110 | 332.6 |
| H200 NVL | NVIDIA | HBM3e 141GB | 417 (835*) | 835 (1,671*) | 1,670 (3,341*) | 30 | 60 | - |
| H100 NVL | NVIDIA | HBM3 94GB | 494 (989*) | 989 (1,979*) | 1,979 (3,958*) | 34 | 67 | - |
| RTX5090 | NVIDIA | GDDR7 32GB | 104.8 (209.6*) | 419 (838*) | 1,676 (3,352*) | 1.64 | 104.8 | 318 |
| Wormhole n300 | Tenstorrent | GDDR6 24GB | ||||||
| H100 | NVIDIA | HBM2e 80GB | 378 (756*) | 756 (1,513*) | 1,513 (3,026*) | 26 | 51 | - |
| A100X | NVIDIA | HBM2e 80GB | 159 (318*) | 318 (636*) | 636 (1,272*) | 9.9 | 19.9 | - |
| A100 | NVIDIA | HBM2e 80GB | 156 (312*) | 312 (624*) | 624 (1,248*) | 9.7 | 19.5 | - |
| L40S | NVIDIA | GDDR6 48GB | 183 (366*) | 362 (733*) | 733 (1,466*) | 1.4 | 91.6 | 209 |
| 6000 Ada | NVIDIA | GDDR6 48GB | 182 (364*) | 364 (729*) | 729 (1,457*) | 1.4 | 91.1 | 210.6 |
| L40 | NVIDIA | GDDR6 48GB | 90.5 (181*) | 181 (362*) | 362 (724*) | 1.4 | 90.5 | 209 |
| A6000 | NVIDIA | GDDR6 48GB | 38.7 (77.4*) | 77.4 (154.8*) | 154.8 (309.7*) | 1.3 | 38.7 | 75.6 |
| 5000 Ada | NVIDIA | GDDR6 32GB | 65.3 (130.5*) | 261 (522*) | 522 (1,044*) | 1 | 65.28 | 151 |
| A5000 | NVIDIA | GDDR6 24GB | 27.8 (55.6*) | 55.6 (111.2*) | 111.2 (222.4*) | 0.9 | 27.8 | 54.2 |
| RTX4090 | NVIDIA | GDDR6 24GB | 82.5 (165*) | 165 (330*) | 660.6 (1,321*) | 1.3 | 82.5 | 191 |
| RTX4090 D | NVIDIA | GDDR6 24GB | 73.54 (147*) | 147 (294*) | 588 (1,176*) | 1.14 | 73.54 | 170 |
| RTX4080 | NVIDIA | GDDR6 16GB | 48.7 (97.4*) | 97.4 (195*) | 389.8 (779.8*) | 0.7 | 48.7 | 112.7 |
| RTX4070Ti | NVIDIA | GDDR6 | 40.1 (80.2*) | 80.2 (160.4*) | 320 (641*) | 0.6 | 40.1 | 93 |
| MI250 | AMD | HBM2e 128GB | 45.3 (90.5**) | 362.1 | 362.1 | 90.5 | 45.3 | - |
| MI210 | AMD | HBM2e 64GB | 22.6 (45.3**) | 181 | 181 | 45.3 | 22.6 | - |
| MI100 | AMD | HBM2 32GB | 23.1 | 92.3 | 92.3 | 11.5 | 23.1 | - |
| RX7900XTX | AMD | GDDR6 24GB | 61 | 123 | 123 | 3.8 | 61 | - |
Full support for 10G and IPMI
Definitely from the basics. Support two 10G Ethernet ports and one IPMI port.
Support for high-speed NIC
No need to worry about the network with support for high-speed network adapters, including InfiniBand NDR, HDR, EDR, Ethernet 200G, and 100G.
GADGETINI MONITORING


Manage AI efficiency and key metrics. Real-time AI processor temperature monitoring, right on the display. Get notified before heat affects performance.
Like looking inside the server—only easier. Monitor coolant temperature and chassis humidity at a glance for total peace of mind.
Chassis Insights
Internal TemperatureInternal HumidityCoolant Status
Coolant TemperatureCoolant LevelLeak DetectionCPU & AI Processor Status
Chip TemperatureMemory UtilizationPower Consumption
DESIGN


Reliable power in any situation. Every dg5W configuration comes with four enterprise-grade PSUs, delivering over 1300W of power to ensure stable, reliable support for AI processors. With redundant hot-swappable power supplies, critical workloads and data remain secure, even during unexpected situations.

Up to 8 Expansion Storage Drives. Expand beyond the NVMe M.2 main storage with up to eight 2.5" SSDs—delivering a solid 64TB of additional capacity. Hot-swappable bays let you upgrade or replace drives without interruption, while RAID configurations keep your data secure.
deep gadget comes preloaded with all the software needed for AI research and development—so you can start deep learning the moment you power it on. From the OS to the deep learning stack, everything is optimized by the deep gadget team. Designed for effortless compatibility, so you can stay focused on your research.
SPEC
Quietness
Size
W x D x H
CPU
GPU
Memory
HDD
PCIe
OS
Network
Warranty
Power
Manual & Warranty