Nvidia’s DGX Spark—the 1-petaFLOP “AI dev box” built around the GB10 Grace Blackwell superchip—is officially on sale from October 15. Since launch guidance, the sticker has crept up by $1,000 to $3,999. Here’s what changed, what you’re really getting, and how to decide between Spark, a traditional GPU workstation, or the cloud.
What’s new vs the announcement
- Availability: Oct 15 via NVIDIA and OEM partners (Acer, ASUS, Dell, Gigabyte, HP, Lenovo, MSI) plus select retail.
- Price: now $3,999—$1,000 higher than earlier guidance; Tom’s Hardware confirmed the bump alongside on-sale timing.
- Key specs: GB10 Grace Blackwell superchip; up to 1 PFLOP FP4 (with sparsity); 128 GB unified memory; ConnectX-7 200 Gb/s; NVLink-C2C; up to 4 TB local NVMe; curated NVIDIA AI software stack.
Who Spark is for (and who should skip it)
Buy it if you need low-latency local experiments (agent loops, privacy-sensitive datasets) and want a straight path to DGX Cloud/OCI later. Skip it if your work is bursty or batch-heavy and you’ll get better TCO from a conventional RTX workstation or cloud credits.
Strengths and trade-offs
- Strength: Turnkey NVIDIA AI stack (CUDA/TensorRT/NIMs) reduces integration drag.
- Strength: 128 GB unified memory enables sizable finetunes and big-parameter inference with precision tricks.
- Trade-off: Single-node box—elastic scale still lives in the cloud.
- Trade-off: At $3,999 it rubs against well-specced RTX workstations for graphics-leaning workflows.
Positioning against alternatives
- DIY workstation: Better raw raster/graphics $/perf; Spark wins on “it just works” and datacenter-style features.
- Cloud: Spark kills queue time and egress for day-to-day prototyping; rent big clusters only when you truly need scale.
Buying checklist
- Validate frameworks and models against NVIDIA’s stack (CUDA/TensorRT/NIM).
- Check memory fit: 128 GB unified is generous; 200B inference assumes FP4 + sparsity and careful engineering.
- Plan handoff: decide whether final training/deploy lives on DGX Cloud/OCI/on-prem and mirror that pipeline from day one.
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