How much VRAM does Muse-Glimmer-30B need?

Its published quantizations range from 8.31 GiB to 51.90 GiB in weights alone, before KV cache and runtime overhead.

Can I run Muse-Glimmer-30B on a GeForce RTX 3050?

Q: Can I run Muse-Glimmer-30B on a GeForce RTX 3050?

No. No published quantization of Muse-Glimmer-30B fits a GeForce RTX 3050 at any context with q4_0 KV cache.

Not at these settings. No indexed quantization of Muse-Glimmer-30B fits GeForce RTX 3050 at any context we compute, with q4_0 KV. The smallest shipped quantization is 8.31 GiB in weights alone, against 5.58 GiB usable. CPU offload can still run it, slowly.

From the file· weights summed from filesFrom the file· KV computed per layerPredicted· speed and compute buffer

KV cache dtypef16 q8_0 q4_0— 6 GB card, 5.58 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not

Quant	Weights●	4K◐	8K◐	16K◐	32K◐	64K◐	128K◐
BF16	51.90 GiB	52.8	52.8	52.9	52.9	53.0	53.3
Q8_0	30.12 GiB	31.0	31.1	31.1	31.1	31.3	31.5
Q6_K_L	22.41 GiB	23.3	23.4	23.4	23.4	23.6	23.8
Q6_K	21.81 GiB	22.7	22.7	22.8	22.8	22.9	23.2
Q5_K_L	19.50 GiB	20.4	20.4	20.5	20.5	20.6	20.9
Q5_K_M	18.72 GiB	19.6	19.7	19.7	19.7	19.9	20.1
UD-Q5_K_L	18.41 GiB	19.3	19.3	19.4	19.4	19.5	19.8
Q5_K_S	18.11 GiB	19.0	19.0	19.1	19.1	19.2	19.5
UD-Q5_K_M	17.88 GiB	18.8	18.8	18.8	18.9	19.0	19.2
Q4_K_M	17.13 GiB	18.0	18.1	18.1	18.1	18.3	18.5
Q4_K_L	17.05 GiB	18.0	18.0	18.0	18.1	18.2	18.4
Q4_1	16.60 GiB	17.5	17.5	17.6	17.6	17.7	18.0
Q4_0	16.50 GiB	17.4	17.4	17.5	17.5	17.6	17.9
Q4_K_S	15.20 GiB	16.1	16.1	16.2	16.2	16.3	16.6
IQ4_NL	15.12 GiB	16.0	16.1	16.1	16.1	16.3	16.5
IQ4_XS	14.38 GiB	15.3	15.3	15.3	15.4	15.5	15.7
Q3_K_L	13.77 GiB	14.7	14.7	14.7	14.8	14.9	15.1
UD-IQ3_M	13.15 GiB	14.1	14.1	14.1	14.2	14.3	14.5
Q3_K_M	13.00 GiB	13.9	13.9	14.0	14.0	14.1	14.4
UD-IQ3_XXS	12.23 GiB	13.2	13.2	13.2	13.3	13.4	13.6
IQ3_M	12.21 GiB	13.1	13.1	13.2	13.2	13.3	13.6
Q3_K_S	11.91 GiB	12.8	12.8	12.9	12.9	13.0	13.3
Q2_K_L	11.50 GiB	12.4	12.4	12.5	12.5	12.6	12.9
IQ3_XS	11.47 GiB	12.4	12.4	12.4	12.5	12.6	12.8
UD-IQ2_M	11.41 GiB	12.3	12.4	12.4	12.4	12.6	12.8
IQ3_XXS	10.75 GiB	11.7	11.7	11.7	11.8	11.9	12.1
UD-IQ2_XS	10.72 GiB	11.6	11.7	11.7	11.7	11.9	12.1
Q2_K	10.28 GiB	11.2	11.2	11.2	11.3	11.4	11.6
UD-IQ2_XXS	10.01 GiB	10.9	10.9	11.0	11.0	11.1	11.4
IQ2_M	9.93 GiB	10.8	10.9	10.9	10.9	11.1	11.3
IQ2_S	9.35 GiB	10.3	10.3	10.3	10.4	10.5	10.7
IQ2_XS	8.92 GiB	9.8	9.9	9.9	9.9	10.1	10.3
IQ2_XXS	8.31 GiB	9.2	9.2	9.3	9.3	9.4	9.7

●From the file◐Predictedwhat these mean

Figures are GiB of total memory: weights plus KV cache plus compute buffer and backend overhead. Weights and KV are near-exact; the overhead term is modeled. Hover any cell for the breakdown.

Why other calculators disagree

A parameters × bits ÷ 8 estimate ignores two things that dominate at long context. First, the weights themselves are not the nominal rate — quantizations are mixtures, so the real file is consistently larger than the label implies. Second, most of this model's layers cache only a 2,048-token window rather than the full context.