About this article
This article was generated using an automated workflow powered by generative AI. It organizes the new 64GB configuration of the DGX Spark and the positioning of the two-node cluster based on primary information released by NVIDIA on October 2, 2026. No physical hardware testing has been performed.Verification Status: 📘 NVIDIA Official Primary Information Confirmed – Hardware Not Physically Verified
Information Verification Date: October 2, 2026.
NVIDIA has announced the addition of a 64GB unified memory configuration to the DGX Spark. It is scheduled to be available from various manufacturers starting October 23, 2026, with pricing announced to start at $4,999.
What has changed
Even with the 64GB configuration, it retains the GB10 Grace Blackwell Superchip, DGX OS, and the NVIDIA AI software stack, with NVIDIA anticipating local execution of models up to 100 billion parameters.
The key point is not merely that a 64GB version has been released. An option is provided to expand the total memory to 128GB by connecting two units via ConnectX-7 and configuring them with the NVIDIA Sync Cluster Assistant. In NVIDIA's Qwen 3.8 27B tests, the two-node configuration reportedly delivered up to 1.7x the performance of a single unit. Since these are NVIDIA's internal benchmark figures, identical results may not be achieved with local models or quantization methods.
Points to verify for local AI
Whether the target model fits within the 64GB limit
Whether additional headroom is required for context length and concurrent agent execution
Whether keeping data strictly off the cloud is a core requirement
Whether the cost and operational complexity of a two-node cluster are justified
Support status of target runtimes such as Ollama, vLLM, and PyTorch
NVIDIA states that Ollama, vLLM, PyTorch, and other frameworks are supported on the 64GB version as well.
Before purchasing immediately
The release is scheduled for October 23. At this stage, it cannot be treated as currently available for general purchase. Furthermore, maximum parameter count alone does not determine practical speed or output quality. Decisions should be made by evaluating actual model size, KV cache, quantization, and input/output lengths together.
