DGX Spark

OpenClaw in Your Hand: Building a Physical AI Terminal - Lech Kalinowski, Callstack

Jun 28, 2026 · 24:36

Lech Kalinowski presents Vault, a dual-display handheld AI terminal built on an ESP32-S3 that runs a local LLM and OpenClaw agents without cloud reliance. The device pairs a fast OLED live surface with a bistable e-paper content display, offering four modes — shell, assist, control, and an LLM-native RPG — all powered by a single lithium-polymer cell. The backend serves GPT-oss 120b via NVIDIA TensorRT-LLM, keeping inference off the microcontroller. Kalinowski shares engineering war stories: blown OLEDs from unstable power supplies, software I2C issues, and noisy encoders. The RPG mode generates four worlds with NPCs and narrative state tracked by the LLM, not dice or HP. Built in three months with 130 commits, the terminal is intentionally distraction-free, targeting quiet spaces where users want text-first AI interaction.

Running LLMs locally: Practical LLM Performance on DGX Spark — Mozhgan Kabiri chimeh, NVIDIA

Apr 10, 2026 · 10:16

NVIDIA’s Mozhgan Kabiri Chimeh demonstrates that running LLMs locally on the DGX Spark workstation, powered by the GB10 Grace Blackwell superchip and 128GB unified memory, achieves practical performance for models up to 14B parameters. Using a reproducible vLLM benchmarking methodology, she shows that the 14B NVFP4 quantized model delivers 20.19 tokens per second and a time-to-first-token 3.4× faster than the unoptimized 14B base model. The DGX Spark supports the same NVIDIA AI software stack as production environments, enabling local development, fine-tuning, and privacy-sensitive workloads before scaling to the cloud. NVFP4 quantization is highlighted as critical for balancing intelligence and throughput on single-system setups.