# AI Accelerator Development Is Going to be Driven By Its Compute Cost Savings

By Software And Technology · Published 2024-05-16 · Updated 2026-06-09 · Professional AV on MarketScale
Canonical: https://www.marketscale.com/industries/pro-av/ai-accelerator-development-is-going-to-be-driven-by-its-compute-cost-savings
Creator hub: Experts Talk

> Companies racing to cut infrastructure expenses are discovering that on-device AI processing delivers both immediate savings and stronger data protection than c

## Key points

- On-device AI processing reduces infrastructure costs compared to cloud-based compute models.
- Edge AI accelerators offer stronger data protection by keeping processing local rather than transmitting to the cloud.
- Cost savings are becoming a primary driver of AI accelerator adoption and development roadmaps.

Block Field

In an era where energy costs are skyrocketing and data privacy concerns are paramount, AI accelerators are emerging as a game-changing technology. This discussion, a clip from a [full episode](https://marketscale.com/industries/experts-talk/softtech-roundtable/ai-accelerators-enhance-computational-might-across-industries-innovation-and-open-standards-key-for-adoption/) of **MarketScale**'s [Experts Talk](https://marketscale.com/shows/experts-talk/) roundtable series, delves into the transformative potential of AI accelerators in enhancing on-device computing for AI workloads, offering significant cost savings on cloud computing, and ensuring data security. With such substantial benefits, the stakes are high for businesses looking to innovate and stay competitive in the rapidly evolving tech landscape, as well as for the companies pioneering AI accelerator development.

> AI accelerators are emerging as a game-changing technology.

What are the core benefits of AI accelerators, and how can businesses leverage them for cost savings and enhanced data security? And how will these very same cost savings motivate further AI accelerator development?

Experts in Focus:

- [**Joel Polanco**](https://www.linkedin.com/in/jpolanco/), Segment Manager at [**Intel Corporation**](https://www.intel.com/content/www/us/en/topics/overview.html)
- [**Grant Powell**](https://www.linkedin.com/in/grantpowell/), Founder of [**Curios**](https://www.curios.com/about/)
- [**David Fellows**](https://www.linkedin.com/in/dlf00/), Chief Digital Officer at [**Acuity Knowledge Partners**](https://www.acuitykp.com/)

### Key Takeaways

- **Cost Savings on Cloud Computing**: By improving on-device computing, AI accelerators significantly reduce the need for extensive data transfer and processing in the cloud, leading to lower energy consumption and cost savings.
- **Enhanced Data Privacy and Security**: Running AI workloads on devices minimizes the data sent to and from the cloud, thus enhancing privacy and security for users.
- **Efficient Model Design**: Developing AI models that require less computing power can enable these models to run effectively on smaller, less capable processing units found in personal devices, rather than relying on powerful cloud servers.
- **Energy Efficiency**: With energy costs rising, optimizing AI workloads to run on-device can mitigate the financial impact of increased energy consumption, providing a more sustainable solution.
- **Consumer Awareness**: Similar to nutritional information on food labels, tech giants like **GCP**, **Azure**, and **AWS** are beginning to provide transparency on the carbon footprint of their services, helping consumers make more informed decisions about their data usage.

Tags: AI accelerator development, AI accelerators, AI computing, AI cost savings

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