Run your n8n AI workflows on NVIDIA DGX Spark, with no cloud API

Small businesses with privacy, regulatory, or data-sovereignty requirements can run AI workflows locally rather than sending customer information to cloud APIs, using a single NVIDIA DGX Spark to host models for chat, document extraction, transcription, OCR, embeddings, reranking, text-to-speech, and image generation. The proposed stack uses a 35B-parameter mixture-of-experts Qwen model quantized in NVFP4 as the primary text and vision model, selected for memory-bandwidth efficiency, alongside specialized models where needed, all served through vLLM and routed via a single OpenAI-compatible LiteLLM endpoint that n8n can access with team-specific keys. It stresses offline model caching, pinned model commits and container versions, private backend networking, careful shared-memory allocation, and license review to make deployments reproducible and commercially suitable. An example n8n invoice workflow performs local OCR and structured field extraction, with reasoning disabled for faster fixed-format tasks, while other examples include call summarization and retrieval-augmented document question answering. Benchmark results indicate strong text throughput and fast speech processing, but show that image generation competes heavily for the shared GPU and can substantially slow chat, suggesting that image work should be queued or moved to separate hardware as demand grows.

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