LLM benchmarking, GPU workload orchestration backend server | Deepseek-R1, Qwen2.5, Llama3.1 | 4xRTX-5090 inside PRU2500, 2xH100 inside PRU2500, 8xMI210 in SuperMicro
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Updated
Nov 6, 2026 - Python
LLM benchmarking, GPU workload orchestration backend server | Deepseek-R1, Qwen2.5, Llama3.1 | 4xRTX-5090 inside PRU2500, 2xH100 inside PRU2500, 8xMI210 in SuperMicro
Run the Google Colab GPU MCP server on Termux/Android: source-built pydantic-core + maturin, DNS-over-HTTPS fix, one-command installer. Call Colab T4/L4 from opencode, Claude Code, Cursor.
GraphKind — Universes v2: T4 + the universe arc (multilayer, observer matrix, hypergraphs, arities, dual). Paper, engine, frozen results, V1–V19 verification, CC-BY-4.0. DOI 10.5281/zenodo.22747350
DCGAN trained on CelebA to generate 64×64 face images from scratch using PyTorch — with Comet ML experiment tracking
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