MawForge: Memory-Bounded Expert Materialization for Local Mixture-of-Experts Inference
A new paper introduces MawForge, a system that enables practical local inference of Sparse Mixture-of-Experts (MoE) language models on memory-constrained unified-memory machines by storing the model on disk and materializing expert tensors on demand into a bounded cache. The system is effective as a measurement substrate but not as a cache-maximization policy.
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[Submitted on 17 Jun 2026]
Title:MawForge: Memory-Bounded Expert Materialization for Local Mixture-of-Experts Inference
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Abstract:Sparse Mixture-of-Experts (MoE) language models separate total parameter count from per-token active computation, but local inference systems often still require the full model, key-value cache, runtime buffers, and operatingsystem headroom to fit in fast memory. MawForge tests a different systems hypothesis: local MoE serving can be made practical on constrained unified-memory machines by storing the full model on disk, keeping common tensors resident, and materializing routed expert tensors into a bounded execution cache on demand. The central finding is that MawForge is effective as a bounded execution mechanism and measurement substrate for local MoE inference, but not as a cache-maximization policy. Performance depends on balancing expert reuse against resident footprint, KV-cache size, quantization, route locality, and macOS memory pressure.
Comments: 7 pages
Subjects:
Machine Learning (cs.LG)
ACM classes: C.4; D.4.2; I.2.7
Cite as: arXiv:2607.09686 [cs.LG]
(or arXiv:2607.09686v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.09686
arXiv-issued DOI via DataCite
Submission history
From: Craig Opie [view email] [v1] Wed, 17 Jun 2026 00:33:45 UTC (66 KB)
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