The NVIDIA DGX Spark is a compact AI computing system designed to bring supercomputing capabilities to the desktop. It is priced starting at $2,999 for partner models and $3,999 for the Nvidia-branded Founders Edition with 4TB storage[1]. This system features a Blackwell architecture GPU, providing up to 1 petaflop of FP4 AI compute power and 128 GB of LPDDR5X unified memory[2].
In comparison, other high-performance AI GPUs vary significantly in price and performance:
- NVIDIA RTX 4090: Primarily a gaming GPU, it is also capable of handling AI tasks, especially for small to medium-scale projects. The RTX 4090 is generally priced around $1,600 to $2,000, making it a cost-effective option for developers experimenting with deep learning models[3].
- NVIDIA RTX 5090: This GPU introduces the Blackwell 2.0 architecture, offering a significant performance leap over its predecessor. While not yet widely adopted in enterprise environments, its price-to-performance ratio makes it a strong contender for researchers and developers. The price for the RTX 5090 is not explicitly mentioned but is expected to be higher than the RTX 4090 due to its advanced features and performance[3].
- NVIDIA RTX A6000: A workstation powerhouse with 48 GB of VRAM and ECC support, making it perfect for training large models. The RTX A6000 is generally more expensive than consumer GPUs like the RTX 4090, typically priced around $4,000 to $6,000, depending on the vendor and specific configuration[3].
- NVIDIA A100: Designed for data centers and high-performance computing environments, the A100 is a top choice for enterprise AI applications. It offers exceptional processing capabilities and up to 80 GB of HBM2e memory. However, its cost is substantial, placing it beyond the reach of individual researchers or smaller organizations, typically priced around $10,000 to $15,000 per unit[5].
- NVIDIA H100: Dominates the AI training sector with its Hopper architecture and enhanced memory bandwidth. It is ideal for large-scale AI models and is priced similarly to the A100, around $10,000 to $15,000 per unit, depending on the configuration and vendor[3].
In summary, the DGX Spark offers a unique blend of performance and accessibility for AI computing at a relatively lower cost compared to high-end data center GPUs like the A100 and H100. However, it is more expensive than consumer-grade GPUs like the RTX 4090, reflecting its specialized design for AI workloads and inclusion of Nvidia's custom software tools.
Citations:
[1] https://www.pcmag.com/news/what-is-nvidias-dgx-station-a-new-specialized-desktop-line-for-ai-work
[2] https://www.youtube.com/watch?v=krBh0Von-2A
[3] https://www.gpu-mart.com/blog/best-gpus-for-ai-and-deep-learning-2025
[4] https://www.trgdatacenters.com/resource/nvidia-dgx-buyers-guide-everything-you-need-to-know/
[5] https://blog.spheron.network/the-top-5-ai-gpus-of-2025-powering-the-future-of-intelligence
[6] https://www.amax.com/comparing-nvidia-blackwell-configurations/
[7] https://www.reddit.com/r/LocalLLaMA/comments/1jef1dd/dgx_spark_previously_digits_has_273gbs_memory/
[8] https://www.tomshardware.com/reviews/gpu-hierarchy,4388.html
[9] https://www.reddit.com/r/LocalLLaMA/comments/1jedlum/dgx_sparks_nvidia_digits/