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№ 002·Hardware·6 min read·Q2 2026

One Spark, dual Spark, or custom RTX PRO: which path, when

When one Spark is enough, when linking two enables frontier models, and when a custom RTX PRO workstation or multi-GPU server is the honest recommendation.

Choosing local AI hardware comes down to three variables: model footprint (VRAM/Unified Memory), token generation speed, and required user concurrency.

A single NVIDIA DGX Spark with 128GB Unified Memory offers a compact desktop footprint with 240W peak power. It comfortably hosts 27B to 35B dense models and active MoEs like Qwen3.8-27B or Qwen3.5 122B A10B at NVFP4/BF16, serving 8 to 32 concurrent interactive users.

When your workload demands frontier reasoning models like DeepSeek V4 Flash (142GB footprint) or GLM 5.2, a single box runs out of memory headroom. Linking two DGX Spark nodes over ConnectX doubles memory to 256GB, enabling 160 to 280 tok/s across 16 to 64 sessions.

For creative video/image generation, high PCI bandwidth, or custom system modularity, dedicated RTX PRO 6000 workstations with up to 384GB VRAM provide an alternative architecture.

Our assessment measures your actual prompts, document lengths, and user traffic to recommend the exact hardware path before any purchase order is issued.