엔비디아 DGX 스파크

Leveraging or developing extremely large AI models traditionally requires data center GPU servers, leading to significant cloud cost burdens, latency issues, and data security concerns. Researchers and developers have had to rely on the cloud or build workstations weighing dozens of kilograms.

Especially in film VFX, game development, and AI research, real-time high-performance computing is essential, but this has required enormous equipment that realistically cannot fit on a personal desk. Furthermore, the cloud environment has limitations in handling sensitive data or repeating experiments, which degrades development productivity.

DGX Spark emerged with the concept of"Desktop Data Center"to solve these AI computing accessibility issues – that is, to allow anyone to run data center-level AI computations on their desk.

The biggest differentiator of DGX Spark is that, despite its ultra-compact size that fits in the palm of your hand,1 PetaFLOPSof AI processing performance. This is a reduction of supercomputer-level performance to a workstation scale, made possible by NVIDIA's latest Grace-Blackwell Superchip (next-generation CPU+GPU integrated chip) and 128GB of integrated memory.

This device comes pre-installed with NVIDIA's entire AI software stack, allowing developers to immediately run/tune large-scale models with plug and play functionality. For example, a single DGX Spark can run the latest open-source LLM with approximately 100 billion parameters locally, and process large AI tasks such as video generation in real-time without the cloud.

It also processes video AI up to 8x faster than existing creative equipment like MacBooks, allowing Spark to offload heavy tasks while users work seamlessly on their PCs.

In short, DGX Spark is the only product in its class that provides data center-level GPU performance + large-capacity memory + AI-optimized SW stack in a desktop form factor.

AI developers, researchers, and creators are the core targets. From a corporate perspective, R&D teams at game/film content production companies, university research labs, and startup AI teams are likely to purchase this device.

For example, startups dealing with large-scale models such as graphics or autonomous driving can purchase DGX Spark for research purposes to reduce cloud GPU costs. Government/public research institutions can also adopt it for national research purposes, and wealthy prosumer developers or individual hackers/enthusiasts may also purchase some.

The sales format is mainly B2B high-end workstations, and due to the high price range, it will be focused on companies and institutions (B2B/B2G) rather than general consumers. (For reference, at CES, NVIDIA emphasized this device for developer desktops.)

This product accurately aligns with the increasing trend of AI computing demand and can be expanded to all industries that require high performance. For example, it can also be applied to on-site AI in robotics, medical imaging, and edge computing for autonomous vehicles.

Multiple units can be connected in a modular fashion to create small clusters, which can be expanded as replacements for existing GPU servers in university labs or small and medium-sized business server rooms in the future.

From a regulatory perspective, there are no particular restrictions as it is computer equipment, but the high price may be a limitation on the initial adoption rate. However, NVIDIA already has a portfolio from entry-level Jetson to data center DGX, and Spark fills the gap in between, so it is expected to easily expand to the existing customer base.

In addition, competitors such as AMD are also preparing similar workstations, forming a market category, so DGX Spark will be standardized as a leading product and enjoy more software optimization and peripheral ecosystem expansion.

In conclusion, it is highly likely to be distributed to research and industrial sites around the world due to the explosive increase in AI hardware demand.

In the context of the CES award, DGX Spark received great attention as a product symbolizing the AI boom. Tech media called it a *"data center on your desk"*, praising the emergence of a dream device for AI developers.

NVIDIA has already demonstrated the performance completion through several demos, and surprised the audience at the actual CES site by demonstrating real-time large-scale language model pre-training (processing 250,000 tokens per second).

Market expectations are also very high, with some experts even predicting that it *"will erode the high-performance cloud GPU rental market."* However, price and supply are variables. Because such advanced chips are used, the initial supply may be limited, and the price is expected to be in the tens of thousands of dollars, so it may take time for it to become widespread.

Nevertheless, it is emphasized as NVIDIA's strategic product, as it was introduced as a highlight of Jensen Huang CEO's keynote speech, and the positive evaluation is dominant that it is a *"game changer that will change the landscape of the AI development toolchain."*

Some competitors (AMD) also checked at CES, saying, *"Our small workstation offers performance comparable to DGX Spark at half the price,"* but there is no disagreement that NVIDIA is currently the technology leader.