- Sparkle’s dense PCIe 5.0 design offers a lower-cost entry into AI and data research
- Up to 32 Intel GPUs deliver 768GB of VRAM for scalable AI computing
- 10,800W redundant power system and advanced cooling enable continuous heavy workloads
Taiwanese graphics card maker Sparkle has unveiled its new dense and scalable GPU server that could offer an alternative to expensive AI accelerator systems like Nvidia’s H100, H200 and B200 data center GPUs.
The C741-6U-Dual 16P is designed to hold up to 16 Arc Pro B60 Dual graphics cards, providing a total of 32 Intel GPUs and up to 768GB of combined VRAM.
Each Arc Pro B60 Dual features two Battlemage BMG-G21 GPUs, and when fully equipped, the system will offer a whopping 81,920 GPU cores.
Advanced cooling
The server supports 24GB and 48GB GPU models, paired with 4th or 5th generation Intel Xeon Scalable processors.
It uses Intel’s C741 chipset and supports up to 32 DDR5 memory slots, allowing for 256GB per module for intensive workloads.
Sparkle has built the system around a 6U chassis with plenty of power and cooling capacity.
The top configuration uses five 2700W titanium power supplies, achieving a combined output of 10800W, while the smallest version includes four 2400W units with a power rating of 7200W.
With up to 15 fans installed, the system has clearly been built to manage heat effectively during continuous workloads.
Each GPU connects to the CPU via a PCIe 5.0 x8 interface, either directly or via a Microchip switch, maximizing data bandwidth and avoiding potential bottlenecks caused by shared links.
There is support for dual M.2 slots and multiple NVMe and SATA bays for local storage.
While there’s no way to know at this stage how the Arc Pro B60 will perform on large-scale training or inference tasks, the flexibility the hardware offers could appeal to researchers and developers looking for affordable parallel computing power.
As expected, Sparkle has not revealed pricing for the C741-6U-Dual 16P, but potential buyers can make a purchase inquiry on the website.
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