RunNburn – Run a 295B Moe from a 98GB GGUF on a 64GB RAM Desktop

In plain words

RunNburn is a Rust-based inference engine for running quantized GGUF models larger than available system memory. It keeps model weights file-backed using memory mapping while enforcing an explicit RAM budget, and sizes GPU caches based on detected available VRAM rather than device presets. Designed for developers and researchers, it requires no conversion steps or cache files, treating the GGUF file on disk as the single source of truth. The engine enables running very large models like sparse mixture-of-experts models on desktop hardware by loading only needed components on demand.

written from the facts on this page · September 2026

From the sources

Memory-aware GGUF inference runtime with OpenAI-compatible serving - coderredlab/runNburn

In the maker’s words, at launch

runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name…

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