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Nvidia Introduces a 64GB DGX Spark Starting at $4,999 as the 128GB Model's Price Rises to $6,950

Nvidia's 64GB DGX Spark ships October 23 from partners from $4,999, with unchanged bandwidth and clustering, while the 128GB model's price rose to $6,950 amid a memory shortage.

Hardware & Semiconductors GPUs
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Overview

Nvidia is adding a lower-memory configuration of its DGX Spark desktop AI system. According to The Register, the new GB10-based systems have half the memory and storage of the existing model, are sold exclusively through hardware partners including Acer, Asus, Dell, Gigabyte, HP, and MSI, and are expected to retail for around $4,999. The same report says Nvidia raised the price of the 128 GB DGX Spark to $6,950, an increase of nearly 75 percent from a year earlier, as memory prices climbed.

What We Know

  • Availability and price. Nvidia’s blog says DGX Spark 64GB is available on Friday, Oct. 23, starting at $4,999. Tom’s Hardware likewise reports that partner systems are slated to start at $4999 when they launch on October 23.
  • Hardware. Per Nvidia, the configuration keeps the GB10 Grace Blackwell Superchip, DGX OS and the full NVIDIA AI software stack, the same as the 128GB model. The Register says memory bandwidth is unchanged at 273 GB/s, which it takes to mean the systems use lower-capacity LPDDR5x modules rather than fewer of them, and that the 20-core Arm processor from MediaTek carries over.
  • Model capacity. Nvidia says the 64GB system supports up to 100-billion-parameter models on device. Tom’s Hardware notes that highly intelligent dense models can now fit within 32GB of RAM, albeit with limited context.
  • Clustering. According to Nvidia, two units can connect directly with a QSFP cable, and in Nvidia’s Qwen 3.8 27B test, two clustered 64 GB systems delivered up to 1.7x the performance of a single system. The Register adds that the onboard ConnectX-7 networking enables clustering of up to 4x GB10-based devices at 200 Gbps apiece.
  • Software tooling. Nvidia says its Sync Cluster Assistant detects connected units, validates device configuration and configures the ConnectX-7 network, and that a Sync Model Launcher coming at the end of the month will download and launch Qwen3.8 27B on a single system or a cluster. The Register says that setting up clusters and inference deployments previously required some tinkering in the CLI along with tweaks to vLLM or TensorRT-LLM launch commands. Nvidia lists Ollama, vLLM, and PyTorch with CUDA as supported out of the box.

Why the Smaller Configuration

The Register reports that the 64 GB model is not as well suited to certain AI workloads such as fine tuning, and that Nvidia argues models in the 26-35 billion parameter range, Qwen 3.8 27B for example, are now good enough for local inference powering private agents. Tom’s Hardware frames the logic in terms of cost: skyrocketing RAM prices mean a chip permanently paired with a large amount of costly LPDDR5X is more of a barrier to entry than an asset.

Tom’s Hardware also says that, assuming a 128GB GB10 system can be found in stock, buyers can expect to pay roughly $7000 to $9000 for one right now, above Nvidia’s adjusted list price.

Competition

The Register notes that AMD’s newly launched Ryzen AI Max+ 495 line offers memory capacities from around 32 GB to 192 GB, and that systems built on that chip currently retail for less than Nvidia’s 128 GB DGX Spark. The outlet adds that its own testing has repeatedly shown the GB10’s GPU offers substantially higher performance for AI tasks.

The move follows Nvidia’s earlier work on local inference tooling, which The Machine Herald previously reported when the company released an open-source tool for pooling local AI inference across a home network.

What We Don’t Know

  • Final street prices for the 64GB systems from each partner; The Register and Tom’s Hardware both describe $4,999 as an expected or starting figure, and Tom’s Hardware cautions that prices may not stay there.
  • Whether the memory shortage will force further price changes before the October 23 launch.
  • How the 64GB configuration performs outside Nvidia’s own Qwen 3.8 27B clustering test, which is the only performance figure Nvidia supplies in its announcement.