Nvidia’s tiniest Grace-Blackwell workstation is lastly making its strategy to retailer cabinets this week, the higher a part of a yr after the GPU big first teased the AI mini PC, then known as Undertaking Digits, at CES.
Since rebranded because the DGX Spark, the roughly NUC-sized system pairs a Blackwell GPU able to delivering as much as a petaFLOP of sparse FP4 efficiency with 128 GB of unified system reminiscence and 200 Gbps of high-speed networking.
However with a value beginning round $3,000, small doesn’t suggest low cost. Then once more, it isn’t precisely aimed toward mainstream PC patrons. The techniques, which will even be obtainable underneath varied model names from OEM companions, will not even include Home windows. A Copilot+ PC this isn’t. As a substitute, it ships with a customized spin of Ubuntu Linux.
Spark is definitely meant for AI and robotics builders, knowledge scientists, and machine studying researchers on the lookout for a lower-cost workstation platform that is nonetheless able to working fashions as much as 200 billion parameters in dimension.
These sorts of workloads are extremely memory-hungry, which makes working them on client graphics processors impractical. Excessive-end workstation playing cards, just like the RTX Professional 6000, may be had with as much as 96 GB of speedy GDDR7, however a single card will set you again greater than $8,000, and that is earlier than you think about the remainder of the platform price.
On the time of launch, the DGX Spark is technically Nvidia’s highest capability workstation GPU — not less than till its Blackwell Extremely-based DGX Station makes its debut.
Honey, I shrunk the superchip
Powering the DGX Spark is the GB10 system-on-a-chip, which is basically a miniaturized model of the Grace-Blackwell Superchips that energy its flagship NVL72 rack techniques.
This is an exploded view of Nvidia’s miniturized Grace Blackwell workstation – Click on to enlarge
As we explored again at Sizzling Chips, the GB10 consists of two compute dies related at 600 GB/s through Nvidia’s proprietary NVLink chip-to-chip interconnect tech. And, in case you are questioning, this identical expertise will ultimately be used to mesh Nvidia’s GPUs to Intel’s future consumer CPUs as a part of a tie-up between the 2 chip heavyweights.
The GPU tile is able to delivering as much as a petaFLOP of sparse FP4 or round 31 teraFLOPS at single precision (FP32) — placing it on par with an RTX 5070 when it comes to uncooked efficiency. Sure, the $550 client card does provide greater than twice the reminiscence bandwidth, however with simply 12 GB of GDDR7, you will be pretty restricted when it comes to what fashions and AI workloads you’ll be able to run.
Not like Nvidia’s authentic Grace CPU, the GB10’s CPU tile is not utilizing Arm’s Neoverse V2 cores. As a substitute, the chip was designed in collaboration with MediaTek and options 20 ARMv9.2 cores. Ten of these are Arm’s high-performance X925 cores, whereas the remaining are primarily based on its efficiency-optimized Cortex A725 cores.
Very like Apple’s M-series and AMD’s Strix Halo SoCs, each the GB10’s CPU and GPU are fed by a typical pool of LPDDR5x. This tight coupling of compute and reminiscence has allowed these chipmakers to attain bandwidths greater than twice that of typical PC platforms in the present day. Within the case of the GB10, Nvidia is claiming 273 GB/s of reminiscence bandwidth.
Scaling out
One factor you will discover on the Spark that you simply will not discover on different techniques is high-speed networking. Similar to Nvidia’s datacenter platforms, the Spark’s GB10 is accompanied by an built-in ConnectX-7 networking card with a pair of QSFP Ethernet ports out the again.
When you may theoretically use these for high-speed networking, the ports are literally designed to attach two DGX Sparks collectively, successfully doubling its fine-tuning and inferencing capabilities.
On this config, Nvidia says customers will have the ability to run inference on fashions as much as 405 billion parameters at 4-bit precision.
DGX Spark techniques from Nvidia, Acer, Asus, Dell Tech, Gigabyte, HPE, Lenovo, and MSI might be obtainable for buy beginning Oct. 15. ®
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