ArliAI · text · mixture of experts
GLM-4.6-Derestricted-v3
ArliAI/GLM-4.6-Derestricted-v3GLM-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
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| IQ1_S2 shards | 71.07 GiB | 76,307,266,080 | 1.711 | — | bartowski |
| IQ1_M2 shards | 74.10 GiB | 79,564,384,800 | 1.784 | — | bartowski |
| IQ2_XXS3 shards | 82.26 GiB | 88,325,421,728 | 1.980 | — | bartowski |
| IQ2_XS3 shards | 94.10 GiB | 101,043,747,488 | 2.266 | — | bartowski |
| IQ2_S3 shards | 94.86 GiB | 101,853,936,288 | 2.284 | — | bartowski |
| IQ2_M3 shards | 107.14 GiB | 115,044,121,216 | 2.580 | — | bartowski |
| Q2_K4 shards | 118.51 GiB | 127,247,272,736 | 2.853 | — | bartowski |
| Q2_K_L4 shards | 119.21 GiB | 128,005,032,768 | 2.870 | — | bartowski |
| IQ3_XXS4 shards | 132.66 GiB | 142,438,804,288 | 3.194 | — | bartowski |
| IQ3_XS4 shards | 138.01 GiB | 148,186,106,624 | 3.323 | — | bartowski |
| Q3_K_S4 shards | 145.89 GiB | 156,653,153,024 | 3.513 | — | bartowski |
| IQ3_M5 shards | 152.98 GiB | 164,263,234,400 | 3.683 | — | bartowski |
| Q3_K_M5 shards | 153.01 GiB | 164,288,138,080 | 3.684 | — | bartowski |
| Q3_K_L5 shards | 158.25 GiB | 169,917,189,056 | 3.810 | — | bartowski |
| IQ4_XS5 shards | 178.83 GiB | 192,019,450,784 | 4.306 | — | bartowski |
| IQ4_NL6 shards | 188.93 GiB | 202,862,709,728 | 4.549 | — | bartowski |
| Q4_06 shards | 191.50 GiB | 205,621,120,000 | 4.611 | — | bartowski |
| Q4_K_S6 shards | 195.49 GiB | 209,905,863,712 | 4.707 | — | bartowski |
| Q4_K_M6 shards | 202.70 GiB | 217,651,747,840 | 4.880 | — | bartowski |
| Q4_16 shards | 208.76 GiB | 224,153,062,368 | 5.026 | — | bartowski |
| Q5_K_S7 shards | 229.46 GiB | 246,376,750,208 | 5.524 | — | bartowski |
| Q5_K_M7 shards | 236.75 GiB | 254,211,230,816 | 5.700 | — | bartowski |
| Q6_K8 shards | 273.40 GiB | 293,559,577,856 | 6.582 | — | bartowski |
| Q8_010 shards | 353.27 GiB | 379,317,898,496 | 8.505 | — | bartowski |
KV cache by context
computed per layer
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 1.44 GiB | 1.44 GiB | — | 92 / 0 / 0 |
| 8,192 | 2.88 GiB | 2.88 GiB | — | 92 / 0 / 0 |
| 16,384 | 5.75 GiB | 5.75 GiB | — | 92 / 0 / 0 |
| 32,768 | 11.50 GiB | 11.50 GiB | — | 92 / 0 / 0 |
| 65,536 | 23.00 GiB | 23.00 GiB | — | 92 / 0 / 0 |
| 131,072 | 46.00 GiB | 46.00 GiB | — | 92 / 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 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.