Tooling
Developer optimizes llama.cpp for 12GB GPUs with expert-layer caching
A llama.cpp fork prioritizes frequently-used mixture-of-experts layers in VRAM, reaching 26 tokens/sec on an RTX 2060—a 37% improvement over standard CPU offloading.
1 min read
Sourcer/localllama
A developer has released an experimental fork of llama.cpp that reorganizes how mixture-of-experts (MoE) models load layers onto constrained GPUs, targeting the 12GB VRAM segment where dense model inference becomes impractical. The optimization selectively caches the most-active expert layers in VRA...
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Method & sources
- Source type
- Primary publication (lab/vendor blog) — our analysis + implication
- Source link
- r/localllama
- Published
- UTC
- Byline
- By the gotcontext.ai team (editorial standards)
- Correction?
- corrections@gotcontext.ai