The number of accelerators deployed directly affects the overall cost in several ways, primarily because each additional accelerator incurs both fixed and variable expenses related to hardware, power consumption, and operational overhead.
**1. Hardware and Deployment Costs:
Each accelerator device has a commercial purchase cost, and deploying more units increases the total capital expenditure proportionally. For example, in hardware accelerator deployments for machine learning tasks, the total cost is roughly proportional to the number of devices deployed, as the cost includes both the price of each device and the associated power consumption over the deployment cycle[5][7]. This means if you double the number of accelerators, the hardware cost roughly doubles as well.
**2. Power Consumption and Operational Costs:
More accelerators consume more power, which adds to the ongoing operational costs. Power consumption depends on both idle and active usage, and when multiple accelerators are deployed, the cumulative power cost can significantly increase the total deployment cost[5]. Efficient accelerators like FPGAs or embedded GPUs may reduce per-device power costs, but total power cost still scales with the number of units.
**3. Bandwidth and Data Transfer Costs:
Accelerators, especially those used for speeding up data transfers (e.g., in cloud or edge computing environments), often use significant bandwidth. Deploying more accelerators can increase bandwidth consumption, which can lead to higher data transfer fees charged by cloud providers or colocation services[4]. These costs are variable and depend on data volume moved and the hosting environmentâs pricing model.
**4. Economies of Scale and Configuration Impact:
While the accelerator automation engine itself may not add cost, the overall solution cost depends heavily on the deployment configuration and scale. Different configurations can range from low-cost setups (~$30/month) to highly available, multi-datacenter environments costing thousands of dollars per month. Increasing the number of accelerators to meet higher demand or improve performance typically shifts the cost toward the higher end of this spectrum[3][4].
**5. Performance vs. Cost Trade-offs:
Adding more accelerators can improve performance (lower latency, higher throughput), but this comes at the expense of higher capital and operational costs. Choosing the right type and number of accelerators involves balancing performance requirements against budget constraints, considering factors such as device efficiency, workload complexity, and deployment duration[5][7].
In summary, the overall cost increases roughly proportionally with the number of accelerators deployed due to hardware purchase costs, power consumption, and bandwidth usage. However, the exact cost impact depends on the specific accelerator type, deployment environment, and workload characteristics. Efficient planning and selection of accelerators can optimize cost-effectiveness while meeting performance goals.
Citations:
[1] https://aws.amazon.com/global-accelerator/pricing/
[2] https://docs.aws.amazon.com/solutions/latest/modern-data-architecture-accelerator/cost.html
[3] https://aws-samples.github.io/aws-secure-environment-accelerator/latest/pricing/sample_pricing/
[4] https://cyfuture.cloud/kb/storage/are-there-pricing-considerations-for-using-an-accelerator-service-for-faster-data-transfer-in-object-storage
[5] https://www.usenix.org/system/files/hotedge20_paper_zhou-xingyu.pdf
[6] https://www.investopedia.com/terms/a/acceleratortheory.asp
[7] http://www.dre.vanderbilt.edu/~gokhale/WWW/papers/HotEdge20_HWAccelReco.pdf
[8] https://www.phdata.io/blog/what-is-the-cost-to-deploy-and-maintain-a-machine-learning-model/
[9] https://www.numberanalytics.com/blog/guide-to-accelerator-principle-econ
[10] https://www.economicsonline.co.uk/definitions/accelerator_effect.html/