cerebras · text · mixture of experts
GLM-4.5-Air-REAP-82B-A12B
cerebras/GLM-4.5-Air-REAP-82B-A12BGLM-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
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| IQ1_M | 27.38 GiB | 29,397,931,360 | 2.870 | — | bartowski |
| IQ2_XXS | 28.63 GiB | 30,744,892,768 | 3.002 | — | bartowski |
| IQ2_XS | 30.45 GiB | 32,694,457,696 | 3.192 | — | bartowski |
| IQ2_S | 30.78 GiB | 33,046,910,304 | 3.227 | — | bartowski |
| IQ2_M | 32.59 GiB | 34,996,475,232 | 3.417 | — | bartowski |
| Q2_K | 33.28 GiB | 35,732,280,672 | 3.489 | — | bartowski |
| Q2_K_L | 33.84 GiB | 36,338,488,672 | 3.548 | — | bartowski |
| IQ3_XXS | 36.30 GiB | 38,977,807,712 | 3.806 | — | bartowski |
| IQ3_XS | 36.66 GiB | 39,367,886,176 | 3.844 | — | bartowski |
| Q3_K_S | 38.48 GiB | 41,321,153,888 | 4.035 | — | bartowski |
| IQ3_M | 39.93 GiB | 42,875,471,200 | 4.186 | — | bartowski |
| Q3_K_M | 39.93 GiB | 42,879,321,440 | 4.187 | — | bartowski |
| Q3_K_L | 40.10 GiB | 43,054,302,560 | 4.204 | — | bartowski |
| IQ4_XS | 43.76 GiB | 46,987,031,904 | 4.588 | — | bartowski |
| IQ4_NL | 45.37 GiB | 48,714,741,088 | 4.757 | — | bartowski |
| Q4_0 | 45.78 GiB | 49,158,468,960 | 4.800 | — | bartowski |
| Q4_K_S2 shards | 49.03 GiB | 52,645,344,800 | 5.140 | — | bartowski |
| Q4_12 shards | 49.92 GiB | 53,604,529,728 | 5.234 | — | bartowski |
| Q4_K_M2 shards | 52.68 GiB | 56,568,477,216 | 5.523 | — | bartowski |
| Q4_K_L2 shards | 53.11 GiB | 57,029,195,296 | 5.568 | — | bartowski |
| Q5_K_S2 shards | 56.33 GiB | 60,489,217,568 | 5.906 | — | bartowski |
| Q5_K_M2 shards | 59.97 GiB | 64,389,412,384 | 6.287 | — | bartowski |
| Q6_K2 shards | 70.98 GiB | 76,212,086,336 | 7.441 | — | bartowski |
| Q8_03 shards | 84.17 GiB | 90,372,830,592 | 8.824 | — | bartowski |
KV cache by context
computed per layer
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.72 GiB | 0.72 GiB | — | 46 / 0 / 0 |
| 8,192 | 1.44 GiB | 1.44 GiB | — | 46 / 0 / 0 |
| 16,384 | 2.88 GiB | 2.88 GiB | — | 46 / 0 / 0 |
| 32,768 | 5.75 GiB | 5.75 GiB | — | 46 / 0 / 0 |
| 65,536 | 11.50 GiB | 11.50 GiB | — | 46 / 0 / 0 |
| 131,072 | 23.00 GiB | 23.00 GiB | — | 46 / 0 / 0 |
Compare with
same modality, comparable size
Will it run on your card?
full quant x context sweep
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB
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.