NVIDIA DGX Spark 64GB brings a 1-petaFLOP Grace Blackwell desktop for local agents

NVIDIA is giving local AI a larger memory budget. The new DGX Spark has 64GB of unified memory on the GB10 Grace Blackwell superchip and roughly 1 petaFLOP of compute. That is enough to run 30B–35B-parameter open models for agents, fine-tuning and inference without per-token cloud fees. Two units can be linked over ConnectX-7 networking into a 128GB setup for larger workloads. OEM versions come from ASUS, Dell, HP and Lenovo.

NVIDIA paired the hardware with a showcase model: Meta's Muse Glimmer, which scores 51.2 on SWE-Bench Pro with a 131K-token context and is packaged as an NVIDIA NIM. Separately, F5 benchmarks of NIM deployments found that DPU cache-aware routing tripled throughput at peak GPU memory load.

The local-versus-cloud economics are a live debate. On r/LocalLLaMA, a post comparing 32GB-VRAM GPUs under $1,600 drew 444 upvotes, and another thread asked whether frontier APIs are actually cheaper than local inference. DGX Spark targets the buyer who wants a supported, turnkey box rather than secondhand consumer GPUs. It also competes with Apple Silicon Macs with large unified memory.

The challenge is that cloud prices are falling quickly. OpenAI's $0.10 per million cached input tokens and DeepSeek's MIT-licensed V4.1-Flash both narrow the cost case for local hardware. Pricing was not detailed in the sources. Watch street price, availability dates, and whether 30B-class local models are good enough for real agent work compared with frontier APIs.