I host
30+ free APEX MoE quantizations
as independent research. My only local hardware is an
NVIDIA DGX Spark
(122 GB unified memory) — enough for ~30-50B-class MoEs, but
bigger ones (200B+) require rented compute
on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.
APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers (first/last 5) get higher precision, middle layers compress more aggressively. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
In MoE models the routed-expert FFN tensors dominate the weight budget but only ~8/256 experts activate per token, so APEX compresses middle-layer experts hardest while preserving edge layers, attention, and the always-active shared expert.
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