How much VRAM does gemma-4-31B-it need?

Its published quantizations range from 7.95 GiB to 57.20 GiB in weights alone, before KV cache and runtime overhead.

Can I run gemma-4-31B-it on a RTX A400?

Q: Can I run gemma-4-31B-it on a RTX A400?

No. No published quantization of gemma-4-31B-it fits a RTX A400 at any context with q8_0 KV cache.

Not at these settings. No indexed quantization of gemma-4-31B-it fits RTX A400 at any context we compute, with q8_0 KV. The smallest shipped quantization is 7.95 GiB in weights alone, against 3.72 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— 4 GB card, 3.72 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	57.20 GiB	59.2	59.6	60.2	61.6	64.2	69.5
Q8_0	30.87 GiB	32.9	33.2	33.9	35.2	37.9	43.2
Q6_K_L	25.21 GiB	27.2	27.6	28.2	29.6	32.2	37.5
Q6_K	24.89 GiB	26.9	27.3	27.9	29.3	31.9	37.2
Q5_K_L	21.37 GiB	23.4	23.7	24.4	25.7	28.4	33.7
Q5_K_M	21.06 GiB	23.1	23.4	24.1	25.4	28.1	33.4
Q5_K_S	20.03 GiB	22.1	22.4	23.1	24.4	27.0	32.4
Q4_K_L	18.57 GiB	20.6	20.9	21.6	22.9	25.6	30.9
Q4_1	18.41 GiB	20.4	20.8	21.4	22.8	25.4	30.7
Q4_K_M	18.25 GiB	20.3	20.6	21.3	22.6	25.3	30.6
Q4_0	17.16 GiB	19.2	19.5	20.2	21.5	24.2	29.5
Q4_K_S	16.95 GiB	19.0	19.3	20.0	21.3	24.0	29.3
IQ4_NL	16.79 GiB	18.8	19.2	19.8	21.1	23.8	29.1
IQ4_XS	15.98 GiB	18.0	18.3	19.0	20.3	23.0	28.3
Q3_K_L	15.66 GiB	17.7	18.0	18.7	20.0	22.7	28.0
Q3_K_M	14.82 GiB	16.9	17.2	17.9	19.2	21.8	27.2
IQ3_M	14.09 GiB	16.1	16.5	17.1	18.5	21.1	26.4
Q3_K_S	13.34 GiB	15.4	15.7	16.4	17.7	20.4	25.7
IQ3_XS	12.89 GiB	14.9	15.3	15.9	17.3	19.9	25.2
IQ3_XXS	12.09 GiB	14.1	14.5	15.1	16.4	19.1	24.4
Q2_K_L	12.08 GiB	14.1	14.4	15.1	16.4	19.1	24.4
IQ2_M	11.78 GiB	13.8	14.1	14.8	16.1	18.8	24.1
Q2_K	11.76 GiB	13.8	14.1	14.8	16.1	18.8	24.1
IQ2_S	11.25 GiB	13.3	13.6	14.3	15.6	18.3	23.6
UD-IQ3_XXS	11.02 GiB	13.1	13.4	14.1	15.4	18.0	23.4
IQ2_XS	10.71 GiB	12.7	13.1	13.7	15.1	17.7	23.0
IQ2_XXS	10.09 GiB	12.1	12.5	13.1	14.4	17.1	22.4
UD-IQ2_M	10.01 GiB	12.0	12.4	13.0	14.4	17.0	22.3
IQ1_M	9.42 GiB	11.5	11.8	12.4	13.8	16.4	21.7
UD-IQ2_XXS	7.95 GiB	10.0	10.3	11.0	12.3	15.0	20.3

●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 1,024-token window rather than the full context.