ArliAI · text · mixture of experts

GLM-4.5-Air-Derestricted

ArliAI/GLM-4.5-Air-Derestricted

GLM-4.5-Air-Derestricted at Q4_K_M is exactly 73,501,936,704 bytes (68.45 GiB / 73.50 GB) — an effective 5.323 bits per weight, not the nominal 4. Its KV cache at 32K is 5.75 GiB.

From the file· summed from 2 file(s)From the file· KV per layer
Parameters
110B
total, not active
Architecture
glm4moe
46 layers
Context
131,072
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ1_M35.26 GiB37,859,421,0562.742TMNE
IQ1_M35.26 GiB37,859,421,0562.742bartowski
IQ2_XXS36.90 GiB39,621,618,5602.869bartowski
IQ2_XXS36.90 GiB39,621,618,5602.869TMNE
IQ2_XS39.30 GiB42,194,037,6323.056TMNE
IQ2_XS39.30 GiB42,194,037,6323.056bartowski
IQ2_S39.62 GiB42,546,490,2403.081TMNE
IQ2_S39.62 GiB42,546,490,2403.081bartowski
IQ2_M42.02 GiB45,118,909,3123.267bartowski
IQ2_M42.02 GiB45,118,909,3123.267TMNE
Q2_K42.94 GiB46,102,702,9763.339bartowski
Q2_K42.94 GiB46,102,702,9763.339TMNE
Q2_K_L43.50 GiB46,708,910,9763.383bartowski
Q2_K_L43.50 GiB46,708,910,9763.383TMNE
IQ3_XXS2 shards46.89 GiB50,343,066,6883.646bartowski
IQ3_XXS2 shards46.89 GiB50,343,066,6883.646TMNE
IQ3_XS2 shards47.35 GiB50,839,837,7603.682bartowski
IQ3_XS2 shards47.35 GiB50,839,837,7603.682TMNE
Q3_K_S2 shards49.75 GiB53,415,959,6163.868TMNE
Q3_K_S2 shards49.75 GiB53,415,959,6163.868bartowski
IQ3_M2 shards51.67 GiB55,477,787,7124.018TMNE
IQ3_M2 shards51.67 GiB55,477,787,7124.018bartowski
Q3_K_M2 shards51.67 GiB55,481,637,9524.018bartowski
Q3_K_M2 shards51.67 GiB55,481,637,9524.018TMNE
Q3_K_L2 shards52.07 GiB55,910,374,4644.049bartowski
Q3_K_L2 shards52.07 GiB55,910,374,4644.049TMNE
IQ4_XS2 shards56.63 GiB60,806,220,8644.404TMNE
IQ4_XS2 shards56.63 GiB60,806,220,8644.404bartowski
IQ4_NL2 shards58.73 GiB63,064,509,5044.567bartowski
IQ4_NL2 shards58.73 GiB63,064,509,5044.567TMNE
Q4_02 shards59.38 GiB63,761,992,7684.618bartowski
Q4_02 shards59.38 GiB63,761,992,7684.618TMNE
Q4_K_S2 shards63.62 GiB68,310,027,3284.947TMNE
Q4_K_S2 shards63.62 GiB68,310,027,3284.947bartowski
Q4_12 shards64.77 GiB69,546,036,2885.036TMNE
Q4_12 shards64.77 GiB69,546,036,2885.036bartowski
Q4_K_M2 shards68.45 GiB73,501,936,7045.323TMNE
Q4_K_M2 shards68.45 GiB73,501,936,7045.323bartowski
Q4_K_L2 shards68.88 GiB73,962,654,7845.356TMNE
Q4_K_L2 shards68.88 GiB73,962,654,7845.356bartowski

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.72 GiB0.72 GiB46 / 0 / 0
8,1921.44 GiB1.44 GiB46 / 0 / 0
16,3842.88 GiB2.88 GiB46 / 0 / 0
32,7685.75 GiB5.75 GiB46 / 0 / 0
65,53611.50 GiB11.50 GiB46 / 0 / 0
131,07223.00 GiB23.00 GiB46 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 57.87 GiB. The real file is 68.45 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
46
Attention heads
96
KV heads
8
Head dim
128
Hidden size
4096
Vocab
151,552
Sliding window
none
SWA period
MLA
no
Experts
128
Experts per token
8
use_sliding_window

Questions people ask

How much VRAM does GLM-4.5-Air-Derestricted need?
Q4_K_M is exactly 73,501,936,704 bytes (68.45 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is GLM-4.5-Air-Derestricted's KV cache?
5.75 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Is GLM-4.5-Air-Derestricted a mixture-of-experts model?
Yes — 128 experts, 8 routed per token. Every expert must be resident, but only the routed ones are read per token, which is why its memory requirement and its speed behave very differently.
Which quantization of GLM-4.5-Air-Derestricted should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.