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How to run n8n AI workflows on NVIDIA DGX Spark without calling cloud APIs
n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image generation reduces chat speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours.
n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image gene
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Surface Laptop Ultra review: NVIDIA RTX Spark's local AI performance
The author tested Microsoft's Surface Laptop Ultra equipped with the NVIDIA RTX Spark N1X SoC. The top configuration of this machine features a 20-core Grace Arm CPU, a 6144-core Blackwell GPU, and 128GB of unified memory, with a starting price of $2,599.99. In the UL Procyon AI text generation test, it scored 2703, about 33% higher than the IdeaPad Pro 5 with an Intel Core Ultra X9 388H; MLPerf Flux 2 Klein 4B image generation reached 26.97 images per minute, about 2.7 times that of the latter. The author also used a local Qwen3.8-27B to drive the Hermes agent, which completed extracting benchmark charts from Excel and generating a Word report in about five minutes.
The author tested Microsoft's Surface Laptop Ultra equipped with the NVIDIA RTX Spark N1X SoC. The top configuration of this machine features a 20-core Grace Arm CPU, a 6144-core Blackwell GPU, and 128GB of unified memory, with a starting price of $2,599.99. In the UL Procyon AI text generation test, it scored 2703, about 33% higher than the IdeaPad Pro 5 with an Intel Core Ultra X9 388H; MLPerf Flux 2 Klein 4B image generation reached 26.97 images per minute, about 2.7 times that of the latter.
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n8n blog introduces running AI workflows locally on DGX Spark
On October 19, 2026, the n8n blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to handle transcription, OCR, document Q&A, and image generation without calling cloud APIs. The tutorial states that the entire stack exposes an OpenAI-compatible endpoint through LiteLLM, with the main model being the NVFP4 version of Qwen3.6-35B-A3B, achieving a generation speed of 120 tokens/s for a single user and 359 tokens/s combined for eight users. The author's hands-on testing found that image generation reduces conversation speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours. Currently, this content has only the n8n blog as its source.
On October 19, 2026, the n8n blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to handle transcription, OCR, document Q&A, and image generation without calling cloud APIs. The tutorial states that the entire stack exposes an OpenAI-compatible endpoint through LiteLLM, with the main model being the NVFP4 version of Qwen3.6-35B-A3B, achieving a generation speed of 120 tokens/s for a single user and 359 tokens/s combined f
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Google releases image model Nano Banana 2.1
Google has released Nano Banana 2.1, an image generation and editing model that replaces Nano Banana 2, released in February 2026. According to The Decoder, the company says the new model improves on every front and costs about half as much as its predecessor: 1K images drop from 6.70 cents to 3.36 cents, and 4K from 15.10 cents to 7.56 cents. The model is based on Gemini 3.6 Flash and supports 1K, 2K, and 4K resolutions, can process up to 14 reference images at once while keeping 4 characters and 10 objects consistent, and is being rolled out across the Gemini app, AI Mode in Google Search, Google AI Studio, Flow, Stitch, Google Ads, and the Gemini enterprise platform; the older model gemini-3.1-flash-image will be retired on October 29, 2026. On October 7, Krea announced that Nano Banana 2.1 was live, saying it understands prompts better, edits more sharply, and keeps subjects more consistent, with generation support up to 4K. On October 9, an Artificial Analysis evaluation showed that the model ranks three places higher than Nano Banana 2 on the quality-versus-price frontier, at only half the price.
Google has released Nano Banana 2.1, an image generation and editing model that replaces Nano Banana 2, released in February 2026. According to The Decoder, the company says the new model improves on every front and costs about half as much as its predecessor: 1K images drop from 6.70 cents to 3.36 cents, and 4K from 15.10 cents to 7.56 cents. The model is based on Gemini 3.6 Flash and supports 1K, 2K, and 4K resolutions, can process up to 14 reference images at once while keeping 4 characters a
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Google has released Nano Banana 2.1, an image generation and editing model that replaces Nano Banana 2, released in February 2026. According to The Decoder, the company says the new model improves on every front and costs about half as much as its predecessor: 1K images drop from 6.70 cents to 3.36 cents, and 4K from 15.10 cents to 7.56 cents. The model is based on Gemini 3.6 Flash and supports 1K, 2K, and 4K resolutions, can process up to 14 reference images at once while keeping 4 characters and 10 objects consistent, and is being rolled out across the Gemini app, AI Mode in Google Search, Google AI Studio, Flow, Stitch, Google Ads, and the Gemini enterprise platform; the older model gemini-3.1-flash-image will be retired on October 29, 2026. On October 7, Krea announced that Nano Banana 2.1 was live, saying it understands prompts better, edits more sharply, and keeps subjects more consistent, with generation support up to 4K. On October 9, an Artificial Analysis evaluation showed that the model ranks three places higher than Nano Banana 2 on the quality-versus-price frontier, at only half the price.
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Elorian Releases DecoyCount Benchmark: Frontier Models All Score Below 50% on Visual Counting Accuracy
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each took the average of three runs. Elorian CEO Andrew Dai said the benchmark examines both vision and reasoning, and can distinguish whether a model failed to see the objects or miscounted them.
Elorian and Mercor jointly released the DecoyCount benchmark, which tests whether models truly count objects or rely on visual shortcuts. Frontier models all scored below 50% accuracy, with the highest being GPT-6 Astra at 43.17%. Elorian says its visual reasoning model Glimpse scored 50.48% in a single run on 1,660 questions, making it the only model to exceed 50%, but this result comes from internal testing and did not use an agent or tool framework, whereas the six models in the paper each to
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Simon Willison built Battle Drones, an audio-networked RTS game, with Claude Code
Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchronize game state via microphone audio, and because the audio caused headaches, he added online multiplayer based on a WebSockets relay server.
Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchroniz
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