NVIDIA DGX Spark Review: First PC Is Awesome
NVIDIA DGX Spark: World's smallest AI supercomputer with 128GB memory. Handles 200B+ parameter models, perfect for local AI development and inference.
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Nvidia touts its long-awaited DGX Spark as “the world’s smallest AI supercomputer,” priced at $3,000 to $4,000 (depending on configuration and OEM). You might expect this ARM-based mini PC to outperform its lower-priced peers.
However, this machine is far from the fastest GPU in Nvidia’s lineup. It cannot surpass the RTX 5090 in large language model (LLM) inference, fine-tuning, or even image generation—let alone gaming. The DGX Spark, along with an upcoming series of GB10-based systems, can run models that the 5090 or any other consumer-grade graphics card currently on the market cannot handle.
In local AI development, all the FLOPS and memory bandwidth in the world are useless without enough VRAM to get the job done. Anyone who has tried machine learning workloads on consumer-grade GPUs has encountered CUDA out-of-memory errors more than once.
The Spark comes equipped with 128 GB of memory, the largest among all workstation GPUs in Nvidia’s lineup. Nvidia achieves this with LPDDR5x, which is significantly slower than the GDDR7 used in Nvidia’s 50-series GPUs. However, this means this tiny TOPS processor can perform inference on models with up to 200 billion parameters or fine-tune models with up to 70 billion parameters, both at 4-bit precision.
Typically, such workloads require multiple high-end GPUs, costing tens of thousands of dollars. By sacrificing some performance and bandwidth for greater capacity, Nvidia has built a system that may not be the fastest in any single metric but can handle all workloads.
Nvidia is not the first company to build such a system. Apple and AMD already have machines with large amounts of LPDDR5x and wide memory buses, which are highly popular among members of the r/locallama subreddit.