RigSpark launches hardware-aware local AI

RigSpark tackles local AI’s biggest usability problem: knowing what will actually run before wasting time and storage on model downloads.

• –Hardware-aware sizing makes model selection practical for developers with mismatched GPUs, RAM, or Apple unified memory.
• –It orchestrates existing runtimes instead of replacing them, giving Ollama, llama.cpp, MLX, LM Studio, and BitNet a common workflow.
• –SHA-256 verification, loopback-only services, and offline recommendations make its privacy story stronger than cloud-first AI tools.
• –Image and video generation still depend on a local ComfyUI setup, while performance figures are estimates rather than benchmarks.
• –The open-source, cross-platform approach could make local inference more approachable without hiding the underlying hardware tradeoffs.