ArliAI · text · mixture of experts

GLM-4.6-Derestricted-v3

ArliAI/GLM-4.6-Derestricted-v3

GLM-4.6-Derestricted-v3 at Q4_K_M is exactly 217,651,747,840 bytes (202.70 GiB / 217.65 GB) — an effective 4.880 bits per weight, not the nominal 4. Its KV cache at 32K is 11.50 GiB.

From the file· summed from 6 file(s)From the file· KV per layer
Parameters
357B
total, not active
Architecture
glm4moe
92 layers
Context
202,752
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ1_S2 shards71.07 GiB76,307,266,0801.711bartowski
IQ1_M2 shards74.10 GiB79,564,384,8001.784bartowski
IQ2_XXS3 shards82.26 GiB88,325,421,7281.980bartowski
IQ2_XS3 shards94.10 GiB101,043,747,4882.266bartowski
IQ2_S3 shards94.86 GiB101,853,936,2882.284bartowski
IQ2_M3 shards107.14 GiB115,044,121,2162.580bartowski
Q2_K4 shards118.51 GiB127,247,272,7362.853bartowski
Q2_K_L4 shards119.21 GiB128,005,032,7682.870bartowski
IQ3_XXS4 shards132.66 GiB142,438,804,2883.194bartowski
IQ3_XS4 shards138.01 GiB148,186,106,6243.323bartowski
Q3_K_S4 shards145.89 GiB156,653,153,0243.513bartowski
IQ3_M5 shards152.98 GiB164,263,234,4003.683bartowski
Q3_K_M5 shards153.01 GiB164,288,138,0803.684bartowski
Q3_K_L5 shards158.25 GiB169,917,189,0563.810bartowski
IQ4_XS5 shards178.83 GiB192,019,450,7844.306bartowski
IQ4_NL6 shards188.93 GiB202,862,709,7284.549bartowski
Q4_06 shards191.50 GiB205,621,120,0004.611bartowski
Q4_K_S6 shards195.49 GiB209,905,863,7124.707bartowski
Q4_K_M6 shards202.70 GiB217,651,747,8404.880bartowski
Q4_16 shards208.76 GiB224,153,062,3685.026bartowski
Q5_K_S7 shards229.46 GiB246,376,750,2085.524bartowski
Q5_K_M7 shards236.75 GiB254,211,230,8165.700bartowski
Q6_K8 shards273.40 GiB293,559,577,8566.582bartowski
Q8_010 shards353.27 GiB379,317,898,4968.505bartowski

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0961.44 GiB1.44 GiB92 / 0 / 0
8,1922.88 GiB2.88 GiB92 / 0 / 0
16,3845.75 GiB5.75 GiB92 / 0 / 0
32,76811.50 GiB11.50 GiB92 / 0 / 0
65,53623.00 GiB23.00 GiB92 / 0 / 0
131,07246.00 GiB46.00 GiB92 / 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 186.91 GiB. The real file is 202.70 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
92
Attention heads
96
KV heads
8
Head dim
128
Hidden size
5120
Vocab
151,552
Sliding window
none
SWA period
MLA
no
Experts
160
Experts per token
8
use_sliding_window

Questions people ask

How much VRAM does GLM-4.6-Derestricted-v3 need?
Q4_K_M is exactly 217,651,747,840 bytes (202.70 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-4.6-Derestricted-v3's KV cache?
11.50 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-4.6-Derestricted-v3 a mixture-of-experts model?
Yes — 160 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-4.6-Derestricted-v3 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.