Can I run Nex-N2-Pro on a Apple M5 Max?

Not at these settings. No indexed quantization of Nex-N2-Pro fits Apple M5 Max at any context we compute, with f16 KV. The smallest shipped quantization is 76.16 GiB in weights alone, against 44.64 GiB usable. CPU offload can still run it, slowly.

From the file· weights summed from filesFrom the file· KV computed per layerPredicted· speed and compute buffer
KV cache dtypef16q8_0q4_064 GB card, 44.64 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not
QuantWeights4K8K16K32K64K128K
Q8_0392.56 GiB393.3393.4393.6394.1395.0396.9
Q6_K318.87 GiB319.6319.7319.9320.4321.3323.2
Q5_K_M263.80 GiB264.5264.6264.9265.3266.3268.2
Q5_K_S255.18 GiB255.9256.0256.2256.7257.7259.5
Q4_1231.99 GiB232.7232.8233.1233.5234.5236.3
Q4_K_L225.90 GiB226.6226.7227.0227.4228.4230.3
Q4_K_M225.20 GiB225.9226.0226.3226.7227.7229.6
Q4_K_S216.85 GiB217.6217.7217.9218.4219.3221.2
Q4_0209.96 GiB210.7210.8211.0211.5212.4214.3
IQ4_NL209.10 GiB209.8209.9210.2210.6211.6213.4
IQ4_XS197.66 GiB198.4198.5198.7199.2200.1202.0
IQ3_M176.93 GiB177.7177.8178.0178.5179.4181.3
Q3_K_L176.46 GiB177.2177.3177.5178.0178.9180.8
Q3_K_M169.03 GiB169.7169.9170.1170.6171.5173.4
IQ3_XS168.98 GiB169.7169.8170.1170.5171.5173.3
Q3_K_S161.06 GiB161.8161.9162.1162.6163.5165.4
IQ3_XXS154.67 GiB155.4155.5155.7156.2157.1159.0
IQ2_M132.87 GiB133.6133.7133.9134.4135.3137.2
Q2_K_L130.84 GiB131.6131.7131.9132.4133.3135.2
Q2_K129.92 GiB130.6130.8131.0131.5132.4134.3
IQ2_S112.23 GiB113.0113.1113.3113.8114.7116.6
IQ2_XS110.33 GiB111.0111.2111.4111.9112.8114.7
IQ2_XXS99.01 GiB99.799.8100.1100.6101.5103.4
IQ1_M90.02 GiB90.790.991.191.692.594.4
IQ1_S76.16 GiB76.977.077.277.778.680.5
From the filePredictedwhat these mean

Figures are GiB of total memory: weights plus KV cache plus compute buffer and backend overhead. Weights and KV are near-exact; the overhead term is modeled. Hover any cell for the breakdown.

Why other calculators disagree

A parameters × bits ÷ 8 estimate ignores two things that dominate at long context. First, the weights themselves are not the nominal rate — quantizations are mixtures, so the real file is consistently larger than the label implies. Second, the KV cache grows linearly with context and, past about 32K, becomes larger than the weights for many models.