SPECTRA-MoE: Signal-Processing and High-Dimensional Reduction for Local Compression of Frontier Mixture-of-Experts Language Models: A Research Program Targeting Kimi K3-Class Models on 32GB Apple Silicon

Abstract

Frontier large language models increasingly use trillion-scale mixture-of-experts architectures, multimodality, long-context reasoning, and agentic training, but their checkpoints are far too large for general-purpose computers. Kimi K3 is used here as a representative target: a 2.8-trillion-parameter multimodal model with a one-million-token context window and 896 experts, of which 16 are active per token. Even idealized four-bit quantization would require about 1.27 TiB, far beyond the roughly 18 to 22 GiB realistically available for model weights on a 32 GB Apple Silicon system. SPECTRA-MoE 2.0 proposes a signal-processing-based framework for compressing such models through activation-aligned graph transforms, multirate filter-bank coding, expert sharing, joint truncation and quantization, vector quantization, sparse and low-rank repair, spectral distillation, and dynamically loaded refinements. The framework introduces four main ideas: causal expert graphs based on counterfactual expert substitution; router-margin-certified compression that limits routing changes caused by compression error; a progressively refinable, router-addressable model bitstream with a compact resident base and optional expert or task-specific enhancements; and Fisher-shaped error-feedback quantization that redirects compression noise toward less sensitive model directions. The manuscript develops memory bounds, routing-stability guarantees, rate-allocation methods, expert-graph coding objectives, runtime algorithms, and a staged evaluation program using Kimi K2, K2.5, and eventually Kimi K3. The goal is not exact model equivalence, but high task-conditioned capability retention under strict memory and latency constraints on consumer Apple Silicon. Repository: https://github.com/andrew-jeremy/spectra-k3

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