Muse-Glimmer-30B
meta-models/Muse-Glimmer-30BMuse-Glimmer-30B at Q4_K_M is exactly 17,306,324,000 bytes (16.12 GiB / 17.31 GB) — an effective 4.650 bits per weight, not the nominal 4. Its KV cache at 32K is 0.50 GiB, not the 1.63 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| IQ2_XXS | 8.31 GiB | 8,920,818,976 | 2.397 | — | bartowski |
| IQ2_XS | 8.92 GiB | 9,582,585,120 | 2.575 | — | bartowski |
| IQ2_S | 9.35 GiB | 10,040,015,392 | 2.697 | — | bartowski |
| IQ2_M | 9.93 GiB | 10,657,479,200 | 2.863 | — | bartowski |
| UD-IQ2_XXS | 10.01 GiB | 10,746,373,152 | 2.887 | — | unsloth |
| Q2_K | 10.28 GiB | 11,035,579,936 | 2.965 | — | bartowski |
| UD-IQ2_XS | 10.72 GiB | 11,513,104,416 | 3.093 | — | unsloth |
| IQ3_XXS | 10.75 GiB | 11,546,640,928 | 3.102 | — | bartowski |
| UD-IQ2_M | 11.41 GiB | 12,255,421,472 | 3.293 | — | unsloth |
| IQ3_XS | 11.47 GiB | 12,315,026,208 | 3.309 | — | bartowski |
| Q2_K_L | 11.50 GiB | 12,348,891,936 | 3.318 | — | bartowski |
| Q3_K_S | 11.91 GiB | 12,789,199,648 | 3.436 | — | bartowski |
| IQ3_M | 12.21 GiB | 13,106,238,240 | 3.521 | — | bartowski |
| UD-IQ3_XXS | 12.23 GiB | 13,130,658,848 | 3.528 | — | unsloth |
| Q3_K_M | 13.00 GiB | 13,962,519,328 | 3.751 | — | bartowski |
| UD-IQ3_M | 13.15 GiB | 14,122,705,696 | 3.794 | — | unsloth |
| Q3_K_L | 13.77 GiB | 14,782,112,544 | 3.971 | — | bartowski |
| IQ4_XS | 14.38 GiB | 15,435,096,096 | 4.147 | — | bartowski |
| IQ4_NL | 15.12 GiB | 16,238,568,480 | 4.363 | — | bartowski |
| Q4_K_S | 15.20 GiB | 16,320,943,136 | 4.385 | — | bartowski |
| Q4_K_M | 16.12 GiB | 17,306,324,000 | 4.650 | — | bartowski |
| Q4_02 shards | 16.50 GiB | 17,716,659,392 | 4.760 | — | bartowski |
| Q4_1 | 16.60 GiB | 17,828,207,648 | 4.790 | — | bartowski |
| Q4_K_L | 17.05 GiB | 18,304,441,120 | 4.918 | — | bartowski |
| Q4_K_M2 shards | 17.13 GiB | 18,387,892,032 | 4.940 | — | meta-models |
| UD-Q5_K_M | 17.88 GiB | 19,194,274,848 | 5.157 | — | unsloth |
| Q5_K_S | 18.11 GiB | 19,440,690,208 | 5.223 | — | bartowski |
| UD-Q5_K_L | 18.41 GiB | 19,768,527,904 | 5.311 | — | unsloth |
| Q5_K_M | 18.72 GiB | 20,105,571,360 | 5.402 | — | bartowski |
| Q5_K_L | 19.50 GiB | 20,935,584,544 | 5.625 | — | bartowski |
| Q6_K | 21.81 GiB | 23,414,418,720 | 6.291 | — | bartowski |
| Q6_K_L | 22.41 GiB | 24,065,821,472 | 6.466 | — | bartowski |
| Q8_0 | 27.58 GiB | 29,612,957,984 | 7.956 | — | unsloth |
| Q8_02 shards | 30.12 GiB | 32,342,004,672 | 8.689 | — | bartowski |
| BF162 shards | 51.90 GiB | 55,725,511,168 | 14.972 | — | unsloth |
| BF162 shards | 51.90 GiB | 55,725,511,360 | 14.972 | — | bartowski |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.15 GiB | 0.20 GiB | 1.39× | 13 / 39 / 0 |
| 8,192 | 0.20 GiB | 0.41 GiB | 2.06× | 13 / 39 / 0 |
| 16,384 | 0.30 GiB | 0.81 GiB | 2.72× | 13 / 39 / 0 |
| 32,768 | 0.50 GiB | 1.63 GiB | 3.24× | 13 / 39 / 0 |
| 65,536 | 0.91 GiB | 3.25 GiB | 3.58× | 13 / 39 / 0 |
| 131,072 | 1.72 GiB | 6.50 GiB | 3.78× | 13 / 39 / 0 |
39 of 52 layers cache only a 2,048-token window rather than the full context, on a period of 4. Figures assume the default configuration; --swa-full disables the saving entirely.
Compare with
Will it run on your card?
Why other calculators give a different number
A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 15.60 GiB. The real file is 16.12 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 1.63 GiB at 32K context where the real figure is 0.50 GiB, because most of this model's layers cache a fixed window rather than the whole context.
Architecture
Questions people ask
- How much VRAM does Muse-Glimmer-30B need?
- Q4_K_M is exactly 17,306,324,000 bytes (16.12 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
- How large is Muse-Glimmer-30B's KV cache?
- 0.50 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.
- Which quantization of Muse-Glimmer-30B 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.