coderredlab/runNburn

Run a 110B on a 2016 PC with 16 GB RAM. Know your tok/s before you download. Placement beats budget: predicts speed + memory fit for any GGUF on your exact hardware, self-calibrates, emits the exact llama.cpp command — or 'quantprobe auto' does it all. Falsification-tested laws; misses published at full size. pip install quantprobe

Smart inference engine that adapts to your hardware to run models faster, even when they don’t fit in memory

Run models larger than your RAM or VRAM by streaming MoE experts from SSD. Built on llama.cpp.

High-speed Large Language Model Serving for Local Deployment

AirLLM 2.8T LLM inference with single 4GB GPU

Run the full 2.78-trillion-parameter Kimi K3 model, DeepSeek V4.1 Flash or GLM-5.3-Flash beyond available RAM by streaming activated weights directly from NVMe. A dependency-free, embeddable C inference engine.

A high-performance, GGUF-native Rust & CUDA inference engine optimized for cold-start latency and real-time 'System 1' agent decision loops.

Runs 405B LLMs on 8GB VRAM

Hybrid GPU + CPU inference for mixture-of-experts models, written in Rust down to the CUDA kernels. A drop-in llama-server.