MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression
KV cache memory is the dominant bottleneck for long-context LLM inference. Existing compression methods each act on a single axis of the four-dimensional KV tensor -- token eviction (sequence), quantization (precision), low-rank projection (head dimension), or cross-layer sharing -- but apply the same recipe to every layer. We show that this homogeneity leaves accuracy on the table: different layers respond very differently to each compression operation, and the optimal per-layer mix of eviction and quantization is far from uniform. We propose MoE-nD, a mixture-of-experts framework that routes each layer to its own (eviction-ratio, K-bits, V-bits) tuple under a global memory budget. An of...
| Authors | Libo Sun, Peixiong He, Po-Wei Harn et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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