An MLX build of moonshotai/Kimi-K3 (2.78 T total,
104 B active) pruned to 160 of 896 routed experts per layer and quantized to 2-bit at group
128, so it runs resident on a 192 GB Apple Silicon machine. In short, about half the memory of the
smallest previously published MLX tier.
Read this before downloading
This is a code-capable model with degraded factual recall. It is not a general-purpose K3.
Measured with greedy decoding, 40 new tokens:
prompt
output
verdict
def merge_intervals(intervals):
intervals = sorted(intervals, key=lambda x: x[0]) → merged = [] → for interval in intervals: → if not merged or merged[-1][1] < interval[0]:
correct
The capital of France is
the capital of France is the capital of France is…
fails
机器学习的基本原理是
理解数据，从数据中学习，学习是，学习，学习… (starts coherently, then loops)
degrades
If you need factual recall or Chinese, use a larger tier. If you want a K3 that writes code on a
192 GB Mac, this is the only build we know of that fits.
size
181.2 GB (57 files)
experts
160 of 896 kept per layer (82% pruned), 2-bit affine, group 128
3.93 tok/s (measured on a CUDA node; Apple Silicon will differ)
routing
top-16 of 160 = 10.0% density (base 1.8%)
Why the factual recall is gone, precisely
A token's MoE output combines its top-k experts with gate weights renormalized to sum to 1, so
the model cannot down-weight an expert it dislikes. With p_j = P(expert j is in a token's top-k),
a kept set S serves Σ_{j∈S} p_j / top_k of the router's demand. We measured this on a 32k-token
calibration set:
kept
random
AIMER (weight-only)
REAP (this build)
ideal
160
17.9%
22.3%
51.7%
54.3%
376
42.0%
48.2%
77.5%
79.6%
At 160 experts a token reaches ~8.3 of the 16 experts it wanted. That is enough for code and not for
facts. Recovering full capability needs ~77% retained mass ≈ 376 experts ≈ 366 GB, which does not
fit 192 GB at MLX's 2-bit floor. So this limitation is arithmetic, not a tuning failure.
Things we tried that did not help, so you don't repeat them:
Lower top_k (16 → 12 → 8 → 6): the France prompt loops identically at every value, and
Chinese gets worse. The damage is missing knowledge, not misweighted survivors.
A weight-only criterion (AIMER): only 1.25× better than random. At 137 experts it retained
19.4% and every prompt looped.
Shared-basis + low-rank factorization (keep all 896 experts as W_base + A_jB_j): closed by
measurement — the shared mean alone has 99.9% relative error and rank 256 still 86%. K3's
experts do not share a basis.
