Computing Provider Income

Swan Chain is a decentralized network that connects computing providers with users requiring computational resources. To foster early network growth and incentivize CPs to join and contribute resources, a dual compensation mechanism has been designed:

1. Universal Basic Income (UBI): Provides CPs with a predictable token income when their resources are underutilized.

2. Paid Jobs: Offers market-priced compensation for computational tasks requested by users.

This mechanism ensures a fair and gradual distribution of tokens to providers, supporting the network's expansion until it reaches a critical mass of user-paid tasks. Importantly, the UBI distribution rate is influenced by the resource usage rate, and CPs earn market-based compensation when engaged in paid jobs.

Total Income

The total daily income $I(x)$for a computing provider on day $( x )$ comprises two components:

• UBI Income $y_{\text{UBI}}(x)$

• Paid Job Income $y_{\text{Paid}}(x)$

$$I(x) = y_{\text{UBI}}(x) + y_{\text{Paid}}(x)$$

Substituting the expressions for $y_{\text{UBI}}(x)$ and $y_{\text{Paid}}(x)$

$$I(x) = A \cdot x^{B} \cdot e^{-C x} \cdot (1 - u(x)) + P_{\text{market}}(x) \cdot u(x)$$

Resource Usage Rate Impact

• When $u(x) = 0$:

  • CP receives full UBI allocation.

  • No income from paid jobs.

• When $u(x) = 1$:

  • All resources are utilized by paid jobs.

  • CP receives full income from paid jobs.

  • No UBI allocation.

• Intermediate Values:

  • CP's income is a combination of UBI and paid job compensation, proportional to resource utilization.

Individual CP's UBI

To calculate the UBI for a single CP, we consider both the resource usage and completion rates of tasks. UBI allocation is conditional on sufficient resource contribution and performance metrics:

(1) UBI Workload Calculation

• Calculate the daily completion rate of a single ECP zk-task: $P_{\text{ECP}}$

• Calculate the completion rate of a single FCP sampling task: $P_{\text{FCP}}$

• Number of GPUs: $N_{\text{ECP}}(GPU_k)$ and GPU types.

• Calculate the total UBI workload:

$$UBI_{\text{total}} = UBI_{\text{ECP}} + UBI_{\text{FCP}}$$

$$UBI_{ECP}=\sum\limits_i (\sum\limits_k N_{ECP,i}(GPU_k) \times f_k)$$

$$UBI_{FCP}=\sum\limits_j (\sum\limits_k N_{FCP,j}(GPU_k) \times f_k) *W_{FCP}$$

(2) Calculating the UBI for a single CP:

As an ECP:

$$UBI_{\text{ECP},i}(x) = \frac{\sum\limits_k N_{\text{ECP},i}(GPU_k) \times f_k \times P_{\text{ECP},i}}{UBI_{\text{ECP}} + UBI_{\text{FCP}}} \times y_{\text{UBI}}(x)$$

As an FCP:

$$UBI_{FCP,i}(x)= \frac{\sum\limits_k N_{FCP,i}(GPU_k) \times f_k \times P_{FCP,i} \times W_{FCP} }{UBI_{ECP}+UBI_{FCP}} \times y_{\text{UBI}}(x)$$

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Conditions for CP to Receive UBI

A CP must meet certain conditions to qualify for UBI:

1. Sufficient Collateral:

$$Collateral_{ECP}= \sum\limits_k N_{ECP}(GPU_k) \times C_{base} \times f_k$$

$$Collateral_{FCP}= \sum\limits_k N_{FCP}(GPU_k) \times C_{base} \times f_k \times W_{FCP}$$

Where:

• $N_{ECP}(GPU_k)$ represents the number of ECP for $GPU_k$

• $C_{\text{base}}$ is the base collateral, with an initial value of 3533 (this value will be dynamically adjusted based on the daily computing units of the entire network; for specific adjustment rules, check here)

• $N_{\text{FCP}}(\text{GPU}_k)$represents the number of $\text{GPU}_k$ in FCP

• $N_{\text{ECP}}(\text{GPU}_k)$ represents the number of $\text{GPU}_k$ in ECP.

• $W_{FCP}$ represents the FCP resource bonus ratio, currently set at a constant value of 1.2

NOTE: The value of $W_{FCP}$, 1.2, means that if the same configuration of servers is deployed for FCP, it will generate 20% more earnings than ECP.

1. Completion of Basic Test Tasks:

• FCP: Sampling task

• ECP: ZK task

1. GPU count and type are also factored into the UBI eligibility.

Exit Mechanism:

• CP Exit Mechanism If a CP wishes to exit, they must set taskType = 100.

  • The CP will no longer receive any tasks and will not incur any collateral deductions.

  • The CP will no longer appear on the current dashboard list.

• CPs can request to withdraw their collateral, but this requires a 7-day confirmation period to ensure settlement before the withdrawal is finalized (first requestWithdraw, followed by confirmRequest after 7 days).

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Swan 2.0: Market-Driven Income

The UBI model described above served as the bootstrap phase (Swan 1.0) that built the initial provider network. Swan 2.0 introduces contribution-based rewards that complement and gradually replace UBI. See SIP-002 for the full proposal.

With the launch of the Inference Cloud, Computing Provider income transitions from UBI-only to a dual-income model where providers earn based on actual work performed.

Dual Income Streams

Under Swan 2.0, providers earn through two complementary channels:

1. Inference Revenue (Stablecoins) — Direct payment in USDC for serving inference requests through the Inference Marketplace

2. Contribution Rewards (SWAN Tokens) — Daily SWAN token rewards allocated proportionally based on contribution score

When paid inference requests generate protocol revenue, the split is:

Recipient	Share
Provider	70% (paid in request currency)
Protocol Treasury	20%
SWAN Buyback & Burn	10%

Contribution Score

Each provider receives a daily Contribution Score that determines their share of the SWAN token reward pool:

$$\text{Contribution\_Score} = W_{inf} \times \text{norm}(\text{inferences}) + W_{tok} \times \text{norm}(\text{tokens}) + W_{up} \times \text{uptime} + W_{qual} \times \text{quality} + W_{div} \times \text{diversity}$$

Where the weights are:

Component	Weight	Description
$W_{inf}$	0.30	Inference volume — number of requests processed
$W_{tok}$	0.25	Token throughput — total input + output tokens
$W_{up}$	0.20	Uptime — 30-day uptime percentage
$W_{qual}$	0.15	Quality — success rate adjusted by latency
$W_{div}$	0.10	Model diversity — number of distinct models served

And the component scores are:

$$\text{norm}(x) = \frac{x}{\max(x_{\text{all providers}})}$$

$$\text{uptime\_score} = \frac{\text{uptime}_{30d}}{100}$$

$$\text{quality\_score} = \text{success\_rate} \times (1 - \text{norm}(\text{avg\_latency}))$$

$$\text{diversity\_score} = \frac{\text{models\_served}}{\text{max\_models\_in\_catalog}}$$

Minimum Contribution Thresholds

To prevent reward fragmentation and gaming:

Threshold	Requirement	Effect if Not Met
Minimum Uptime	80% over 7 days	Excluded from contribution pool
Minimum Inferences	100/week	Reduced to 50% contribution weight
Minimum Success Rate	90%	Reduced to 75% contribution weight
Minimum Online Hours	120 hours/week	Pro-rated availability bonus

3-Phase Transition

The transition from UBI to contribution-based rewards follows an accelerated 3-month timeline:

Phase 1: Hybrid Mode (Month 1)

$$\text{Daily\_Reward} = \text{UBI\_Base} \times 0.75 \times (1 - u) + \frac{\text{Score}_i}{\sum \text{Scores}} \times \text{Contribution\_Pool}$$

Where the Contribution Pool is 25% of the current daily UBI allocation.

Phase 2: Contribution-Weighted UBI (Month 2)

$$\text{Daily\_Reward} = \text{UBI\_Base} \times 0.50 \times \text{availability} + \frac{\text{Score}_i}{\sum \text{Scores}} \times \text{Contribution\_Pool}$$

Where the Contribution Pool increases to 50% and availability requires maintaining 95%+ uptime.

Phase 3: Pure Contribution Mode (Month 3+)

$$\text{Daily\_Reward} = \frac{\text{Score}_i}{\sum \text{Scores}} \times \text{Reward\_Pool} + \text{Availability\_Bonus}$$

Where the Availability Bonus (10% of the daily pool) incentivizes standby capacity, weighted by hardware tier:

Hardware Tier	Multiplier
RTX 3090 / A4000	1.0x
RTX 4090 / A5000 / A6000	1.5x
A100	2.5x
H100	4.0x

Payout Structure

Component	Allocation
Liquid SWAN	80%
Locked SWAN (3-month vesting)	20%

Unified Computing Provider (CP) Role

Under Swan 2.0, the legacy ECP and FCP roles merge into a single Computing Provider (CP) classification. All providers are evaluated equally based on contribution metrics, regardless of their previous role.

Migration path for existing providers:

1. ECPs and FCPs running inference tasks are automatically converted to unified CP role

2. Providers not running inference have a 30-day grace period to onboard to Swan Inference or migrate staked SWAN to SwanFi

3. Hardware requirements: ≥ 24 GB VRAM recommended; ≥ 48 GB VRAM for priority task routing