Research, code and images on the GPU you already own, as one Pixeltable app

A Pixeltable-based workflow demonstrates how research, coding, and image-generation tasks similar to those run by Abhinav Raj on a single 16GB GPU can be organized into searchable, versioned tables rather than separate chat and generation interfaces. The proposed setup uses llama.cpp router mode to switch between local research and coding models, MCP-connected Exa search for research tool calls, and a custom ComfyUI HTTP UDF for FLUX image generation, while storing prompts, outputs, tool results, code checks, seeds, and dependencies for later retrieval or recomputation. The author tested the wiring with a small CPU-based language model and a stub image server rather than the cited production models and hardware, emphasizing that model quality, preset settings, and some workflow details were not verified. Pixeltable’s computed columns and version history allow users to update prompts or models and rerun prior inputs while preserving earlier results, and chained tables can make research notes feed coding and image-generation tasks automatically. The approach has practical limitations, including shared GPU memory, one search-tool round per row, unsupported ComfyUI integration code, potential source hallucinations, and the need to manage local model servers, but it offers a reproducible alternative for recurring self-hosted AI workloads.

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