> For the complete documentation index, see [llms.txt](https://docs.swanchain.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.swanchain.io/core-concepts/token/computing-provider-collateral/collateral-requirement-and-earning-multiplier.md). # Collateral Requirement and Computing Unit The base collateral requirement is **3533 $SWAN**. Different GPU models contribute differently to the network and thus have varying **Computing Unit (CU)** and **Collateral Requirements** Per Card (in $SWAN). ### **About Computing Unit (CU)** CU, or **Computing Unit**, is a fundamental parameter in Swan Chain. It serves as a virtual unit that quantifies the computational resources contributed by **Computing Providers (CPs)**. Additionally, CU plays a critical role in determining the **collateral requirements** for CPs. #### **Purpose of CU** The primary function of CU is to calculate the collateral required for CPs, ensuring they maintain sufficient staking to qualify for **UBI rewards** and **task allocations** on Swan Chain. CPs can check their CU value through the [**Provider Dashboard**](https://provider.swanchain.io/). #### **Collateral Calculation Formula** The required **collateral** for CPs is determined using the following formula: $$ Collateral=CU×Base Collateral $$ where **CU** represents the number of computing units allocated to a CP, and **Base Collateral** is a predefined constant, currently set at **3,533**. #### **Example Calculation** For a CP with a **CU value of 100**, given that the **Base Collateral** is **3,533**, the required collateral would be: $$ 100×3,533=353,300 $$ *** ### About Collateral Requirement The Collateral Requirement Per Card (in $SWAN) provided in the table below are for reference only. These figures may change in real-time based on current GPU prices and market conditions
GPU modelComputing UnitCollateral Requirement Per Card (in $SWAN)
H100 NVL2898924
H100 PCIe2898924
H100 80GB HBM32898924
H100 SXM2898924
H20019.970306.7
A100-SXM4-80GB13.648048.8
A100-PCIE-80GB1242396
Titan RTX828264
A10G828264
A800 PCIE7.325790.9
L40s6.522964.5
L47.125084.3
L406.924377.7
RTX 5090724731
A100 SXM4-40GB724731
A100-PCIE-40GB6.924377.7
A406.623317.8
Tesla P45.920844.7
RTX 6000 Ada Generation5.720138.1
RTX 5000 Ada Generation5.519431.5
RTX A60005.118018.3
A165.118018.3
Quadro P40005.118018.3
RTX A40003.713072.1
RTX 4500Ada3.512365.5
RTX A45003.512365.5
RTX 40902.910245.7
RTX 4090 D2.910245.7
RTX 40802.48479.2
RTX A50002.48479.2
QUADRO P20002.38125.9
RTX 4070 SUPER2.27772.6
A302.27772.6
Q RTX 80002.27772.6
RTX 3090 Ti2.17419.3
Tesla V100-PCIE-32GB27066
Tesla V100-SXM2-32GB27066
Tesla V100S-PCIE-32GB27066
RTX 4070S Ti27066
GTX 1050 Ti27066
Quadro P600027066
RTX 4070 Ti1.86359.4
RTX 3080 Ti1.86359.4
RTX 30901.86359.4
GTX 10501.76006.1
Tesla V100-SXM2-16GB1.65652.8
RTX 40701.65652.8
RTX 4080S1.55299.5
A101.55299.5
RTX 40601.55299.5
GTX 10801.55299.5
Tesla V100-PCIE-16GB1.44946.2
Tesla P401.44946.2
Tesla V100-PCIE-16GB1.44946.2
GH200 SXM1.34592.9
RTX 4060 Ti1.24239.6
GP1001.13886.3
Titan V1.13886.3
RTX 308013533
Q RTX 500013533
Q RTX 400013533
RTX 2080S0.93179.7
RTX 20800.93179.7
Tesla P1000.93179.7
Titan Xp0.93179.7
GTX 1070 Ti0.93179.7
Quadro P50000.93179.7
RTX 2080 Ti0.93179.7
CMP 90HX0.93179.7
Titan X0.82826.4
RTX 3060 Ti0.82826.4
RTX 20700.82826.4
GTX 1080 Ti0.82826.4
RTX 30700.82826.4
RTX 4000 SFF Ada Generation0.72473.1
Tesla K800.72473.1
RTX 3070 Ti0.62119.8
RTX 2060S0.62119.8
RTX 30600.62119.8
Tesla T40.51766.5
GTX 10600.51766.5
RTX 3070 laptop0.51766.5
RTX A20000.51766.5
RTX 3060 laptop0.51766.5
RTX 30500.51766.5
GTX 10700.51766.5
GTX 16600.51766.5
P106-1000.51766.5
GTX 1650 S0.51766.5
GTX 1660 S0.51766.5
GTX 1660 Ti0.51766.5
GTX 16500.51766.5
P104-1000.51766.5
GTX 980 Ti0.51766.5
GTX 9800.51766.5
GTX 750 Ti0.51766.5
GTX 9700.51766.5
GTX 7500.51766.5
GTX 9600.51766.5
**Notes:** 1. GPU model: This column lists different models of GPUs. 2. Computing Unit (CU) : CU refers to the relative computational contribution of each GPU model. A higher CU value indicates that the GPU has a greater capacity for contributing computational resources, which can lead to higher potential earnings. 3. Collateral Requirement Per Card (in $SWAN): This shows the amount of $SWAN tokens required as collateral for each GPU card under normal circumstances. 4. Collateral Requirement Per Card during UBI - 0 (in $SWAN): This represents the amount of $SWAN tokens required as collateral for each GPU card during the initial UBI period (referred to as UBI-0). This amount may differ from the regular collateral requirement.