r/u_SnooMarzipans9093

I’m building Kimari Local AI, an open-source toolkit focused on running local LLMs on older consumer NVIDIA GPUs like the GTX 1060 6GB and GTX 1080 8GB.

The goal is not to claim magic performance or pretend an old GPU can compete with modern hardware.

The goal is more practical:

• make local AI easier to run on hardware people already own
• provide sane GPU profiles for limited VRAM
• support GGUF models through llama.cpp + CUDA
• expose a local OpenAI-compatible API
• add CLI tools for setup, diagnostics, benchmarking and model compatibility
• keep everything local: no cloud, no subscriptions, no telemetry

Current status: v0.1.57-alpha.

What works today:

• CLI commands like doctor, start, status, bench, fit, optimize and pull
• llama.cpp runtime support with CUDA acceleration
• local OpenAI-compatible endpoint
• KimariFit scoring concept: useful intelligence per GiB of VRAM
• GPU profiles for old cards
• Open WebUI / Continue / local agent integration plans
• Hugging Face presence with a demo/checker Space and compatible GGUF model collection

Important clarification:

Kimari is currently the framework, not the final model.

Kimari-4B is planned and under development, but no public weights, adapters or official GGUF files are released yet. For now, Kimari is designed to run compatible existing GGUF models locally.

I’d appreciate technical feedback, especially from people running local models on older hardware.