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GLM-4.5-Air-REAP-82B-A12B

cerebras/GLM-4.5-Air-REAP-82B-A12B

GLM-4.5-Air-REAP-82B-A12B at Q4_K_M is exactly 56,568,477,216 bytes (52.68 GiB / 56.57 GB) — an effective 5.523 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
81.9B
total, not active
Architecture
glm4moe
46 layers
Context
131,072
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ1_M27.38 GiB29,397,931,3602.870bartowski
IQ2_XXS28.63 GiB30,744,892,7683.002bartowski
IQ2_XS30.45 GiB32,694,457,6963.192bartowski
IQ2_S30.78 GiB33,046,910,3043.227bartowski
IQ2_M32.59 GiB34,996,475,2323.417bartowski
Q2_K33.28 GiB35,732,280,6723.489bartowski
Q2_K_L33.84 GiB36,338,488,6723.548bartowski
IQ3_XXS36.30 GiB38,977,807,7123.806bartowski
IQ3_XS36.66 GiB39,367,886,1763.844bartowski
Q3_K_S38.48 GiB41,321,153,8884.035bartowski
IQ3_M39.93 GiB42,875,471,2004.186bartowski
Q3_K_M39.93 GiB42,879,321,4404.187bartowski
Q3_K_L40.10 GiB43,054,302,5604.204bartowski
IQ4_XS43.76 GiB46,987,031,9044.588bartowski
IQ4_NL45.37 GiB48,714,741,0884.757bartowski
Q4_045.78 GiB49,158,468,9604.800bartowski
Q4_K_S2 shards49.03 GiB52,645,344,8005.140bartowski
Q4_12 shards49.92 GiB53,604,529,7285.234bartowski
Q4_K_M2 shards52.68 GiB56,568,477,2165.523bartowski
Q4_K_L2 shards53.11 GiB57,029,195,2965.568bartowski
Q5_K_S2 shards56.33 GiB60,489,217,5685.906bartowski
Q5_K_M2 shards59.97 GiB64,389,412,3846.287bartowski
Q6_K2 shards70.98 GiB76,212,086,3367.441bartowski
Q8_03 shards84.17 GiB90,372,830,5928.824bartowski

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 42.92 GiB. The real file is 52.68 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
96
Experts per token
8
use_sliding_window

Questions people ask

How much VRAM does GLM-4.5-Air-REAP-82B-A12B need?
Q4_K_M is exactly 56,568,477,216 bytes (52.68 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-REAP-82B-A12B'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-REAP-82B-A12B a mixture-of-experts model?
Yes — 96 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-REAP-82B-A12B 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.