Can I run Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-BF16 on a GeForce RTX 2080 Ti?

Not at these settings. No indexed quantization of Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-BF16 fits GeForce RTX 2080 Ti at any context we compute, with f16 KV. The smallest shipped quantization is 10.12 GiB in weights alone, against 10.23 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 dtypef16q8_0q4_011 GB card, 10.23 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not
QuantWeights4K8K16K32K64K128K
Q8_027.05 GiB28.228.428.929.931.935.9
I1-Q6_K20.89 GiB22.022.322.823.825.829.8
Q6_K20.89 GiB22.022.322.823.825.829.8
I1-Q5_K_M18.19 GiB19.319.620.121.123.127.1
Q5_K_M18.19 GiB19.319.620.121.123.127.1
I1-Q5_K_S17.67 GiB18.819.019.520.522.526.5
Q5_K_S17.67 GiB18.819.019.520.522.526.5
I1-Q4_116.15 GiB17.317.518.019.021.025.0
I1-Q4_K_M15.66 GiB16.817.017.518.520.524.5
Q4_K_M15.66 GiB16.817.017.518.520.524.5
I1-Q4_K_S14.74 GiB15.916.116.617.619.623.6
Q4_K_S14.74 GiB15.916.116.617.619.623.6
I1-Q4_014.68 GiB15.816.016.517.519.523.5
IQ4_XS14.36 GiB15.515.716.217.219.223.2
I1-IQ4_XS14.26 GiB15.415.616.117.119.123.1
I1-Q3_K_L13.56 GiB14.714.915.416.418.422.4
Q3_K_L13.56 GiB14.714.915.416.418.422.4
I1-Q3_K_M12.57 GiB13.713.914.415.417.421.4
Q3_K_M12.57 GiB13.713.914.415.417.421.4
I1-IQ3_M11.89 GiB13.013.313.814.816.820.8
I1-IQ3_S11.74 GiB12.913.113.614.616.620.6
I1-Q3_K_S11.41 GiB12.512.813.314.316.320.3
Q3_K_S11.41 GiB12.512.813.314.316.320.3
I1-Q2_K10.12 GiB11.211.512.013.015.019.0
Q2_K10.12 GiB11.211.512.013.015.019.0
From the filePredictedwhat 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, the KV cache grows linearly with context and, past about 32K, becomes larger than the weights for many models.