RotaryQuant: Fitting 120B MoE Models on Consumer Hardware via Fused Compressed-Space Attention
Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident weight matrices, key-value (KV) cache state that grows linearly with context, and dozens of expert sublayers that must be paged on demand. We present RotaryQuant, a three-axis compression system that addresses all three. Mixed-precision weight quantization assigns bit-widths by architectural role: 4-bit for dense layers, 2-bit for routed experts, and 8-bit for the shared expert whose high activation kurtosis resists aggressive compression. LRU expert offloading pages non-resident experts to disk under genuine memory pr...
| Authors | Anthony. Lui, Mohamed. Elsaied, N. P. Savani |
|---|---|
| Primary category | cs.NE |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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