How much VRAM does Qwen2.5-72B need?

Its published quantizations range from 21.13 GiB to 135.44 GiB in weights alone, before KV cache and runtime overhead.

Can I run Qwen2.5-72B on a Apple M3 Pro?

Q: Can I run Qwen2.5-72B on a Apple M3 Pro?

No. No published quantization of Qwen2.5-72B fits a Apple M3 Pro at any context with q4_0 KV cache.

Not at these settings. No indexed quantization of Qwen2.5-72B fits Apple M3 Pro at any context we compute, with q4_0 KV. The smallest shipped quantization is 21.13 GiB in weights alone, against 12.56 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— 18 GB card, 12.56 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◐
F16	135.44 GiB	136.5	136.8	137.5	138.9	141.7	147.4
Q8_0	71.96 GiB	73.0	73.3	74.0	75.4	78.3	83.9
Q6_K	59.93 GiB	61.0	61.3	62.0	63.4	66.2	71.9
Q5_1	50.88 GiB	51.9	52.3	53.0	54.4	57.2	62.8
Q5_K_M	50.71 GiB	51.7	52.1	52.8	54.2	57.0	62.6
Q5_K_S	47.85 GiB	48.9	49.2	49.9	51.3	54.2	59.8
Q5_0	46.86 GiB	47.9	48.2	48.9	50.4	53.2	58.8
I1-Q4_K_M	44.16 GiB	45.2	45.5	46.2	47.7	50.5	56.1
Q4_K_M	44.16 GiB	45.2	45.5	46.2	47.7	50.5	56.1
I1-Q4_1	42.56 GiB	43.6	43.9	44.6	46.1	48.9	54.5
Q4_1	42.56 GiB	43.6	43.9	44.6	46.1	48.9	54.5
I1-Q4_K_S	40.88 GiB	41.9	42.3	43.0	44.4	47.2	52.8
Q4_K_S	40.88 GiB	41.9	42.3	43.0	44.4	47.2	52.8
I1-Q4_0	38.54 GiB	39.6	39.9	40.6	42.0	44.8	50.5
Q4_0	38.54 GiB	39.6	39.9	40.6	42.0	44.8	50.5
I1-IQ4_NL	38.48 GiB	39.5	39.9	40.6	42.0	44.8	50.4
IQ4_NL	38.48 GiB	39.5	39.9	40.6	42.0	44.8	50.4
IQ4_XS	37.40 GiB	38.4	38.8	39.5	40.9	43.7	49.3
I1-IQ4_XS	36.98 GiB	38.0	38.4	39.1	40.5	43.3	48.9
I1-Q3_K_L	36.79 GiB	37.8	38.2	38.9	40.3	43.1	48.7
Q3_K_L	36.79 GiB	37.8	38.2	38.9	40.3	43.1	48.7
I1-Q3_K_M	35.11 GiB	36.1	36.5	37.2	38.6	41.4	47.0
Q3_K_M	35.11 GiB	36.1	36.5	37.2	38.6	41.4	47.0
I1-IQ3_M	33.07 GiB	34.1	34.4	35.2	36.6	39.4	45.0
IQ3_M	33.07 GiB	34.1	34.4	35.2	36.6	39.4	45.0
I1-IQ3_S	32.12 GiB	33.2	33.5	34.2	35.6	38.4	44.0
I1-Q3_K_S	32.12 GiB	33.2	33.5	34.2	35.6	38.4	44.0
IQ3_S	32.12 GiB	33.2	33.5	34.2	35.6	38.4	44.0
Q3_K_S	32.12 GiB	33.2	33.5	34.2	35.6	38.4	44.0
I1-IQ3_XS	30.59 GiB	31.6	32.0	32.7	34.1	36.9	42.5
IQ3_XS	30.59 GiB	31.6	32.0	32.7	34.1	36.9	42.5
I1-IQ3_XXS	29.66 GiB	30.7	31.0	31.7	33.2	36.0	41.6
IQ3_XXS	29.66 GiB	30.7	31.0	31.7	33.2	36.0	41.6
I1-Q2_K	27.76 GiB	28.8	29.1	29.9	31.3	34.1	39.7
Q2_K	27.76 GiB	28.8	29.1	29.9	31.3	34.1	39.7
I1-Q2_K_S	27.54 GiB	28.6	28.9	29.6	31.0	33.8	39.5
Q2_K_S	27.54 GiB	28.6	28.9	29.6	31.0	33.8	39.5
I1-IQ2_M	27.32 GiB	28.4	28.7	29.4	30.8	33.6	39.3
IQ2_M	27.32 GiB	28.4	28.7	29.4	30.8	33.6	39.3
I1-IQ2_S	26.02 GiB	27.1	27.4	28.1	29.5	32.3	38.0
IQ2_S	26.02 GiB	27.1	27.4	28.1	29.5	32.3	38.0
I1-IQ2_XS	25.20 GiB	26.2	26.6	27.3	28.7	31.5	37.1
IQ2_XS	25.20 GiB	26.2	26.6	27.3	28.7	31.5	37.1
I1-IQ2_XXS	23.74 GiB	24.8	25.1	25.8	27.2	30.0	35.7
IQ2_XXS	23.74 GiB	24.8	25.1	25.8	27.2	30.0	35.7
I1-IQ1_M	22.11 GiB	23.1	23.5	24.2	25.6	28.4	34.0
IQ1_M	22.11 GiB	23.1	23.5	24.2	25.6	28.4	34.0
I1-IQ1_S	21.13 GiB	22.2	22.5	23.2	24.6	27.4	33.1
IQ1_S	21.13 GiB	22.2	22.5	23.2	24.6	27.4	33.1

●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 131,072-token window rather than the full context.