Just as businesses are starting to embrace AI en masse, they are facing a brutal reality. Large organizations, including Microsoft and Uber, have highlighted the significant infrastructure costs associated with scaling AI as major service providers move to token-based AI credits, threatening to stifle AI adoption within enterprises by making it unbearably expensive.Every single AI prompt generates an inference request, and as usage scales across organisations, the cumulative cost of those requests is becoming increasingly difficult to ignore.
The early days of free, unlimited AI usage within businesses gave way to token-maxxing and, subsequently, token-shaming, where employees are audited, and caps are introduced on prompts. And it’s only starting.
OpenAI's two-month Codex trial and Anthropic's 50% capacity boost both expire in four weeks. Receipts will start coming in in August, from September onwards, and CFOs and finance directors will very likely find themselves scrambling to contain ballooning AI budgets.
As organisations move beyond AI experimentation and into production-scale deployments, the economics of AI are becoming increasingly important. In a strange parallel with cloud computing, we’re starting to see a common thread emerging.
Driven by surging token costs, unpredictable consumption models, and growing concerns around data sovereignty, companies are investigating local AI, the process of running AI inference on-premises or in a private cloud.
How the ASUS Turbo Radeon AI Pro R9700 supports local AI workloads
Businesses need hardware solutions that match their use cases, and that’s what the ASUS Turbo Radeon AI Pro R9700 is. A compact, powerful workstation-class AI-native GPU from the world’s largest video card manufacturer.
The ASUS Turbo Radeon AI Pro R9700, with its 32GB of VRAM and support for multi-GPU deployments, is engineered specifically for these memory-intensive inference workloads.
For enterprises focused on AI inference rather than model training, GPU memory capacity is becoming a critical consideration. Running larger language models locally allows organisations to reduce dependence on consumption-based APIs while maintaining faster response times and greater oversight of proprietary information
And memory is becoming increasingly important as inference becomes the next stepping stone in the AI revolution. Compared to more expensive rivals with less memory, the Radeon AI Pro R9700 absolutely trounces them. According to AMD testing, the Radeon AI Pro R9700 delivered up to five times higher performance in specific Qwen 3 32B inference scenarios compared with the GeForce RTX 5080.
Having not enough RAM on your laptop slows it down. With LLMs, low memory impacts the delivery speed and accuracy! You want to use the model with the most parameters, but have to compromise on speed.
Going for the largest RAM capacity for one’s budget and opting for a scalable GPU solution makes perfect sense. Foolproofing such an investment is a no-brainer, especially given the current economic climate.
Why local AI improves data sovereignty, governance and privacy
Running a local AI infrastructure delivers better control to enterprises. This comes amidst a shift in sentiment in the UK and Europe that places governance, sovereignty and privacy at the heart of the AI conversation within businesses and governments.
More emphasis is now placed on where data is located, how AI is processing it, who is accessing it and how it is managed.
Retrieval-Augmented Generation (RAG) enables organisations to securely query proprietary datasets using AI models. Running RAG workloads locally can help reduce external data exposure while supporting compliance requirements.
Having a local AI solution goes a long way to make this sustainable, transparent and trustworthy. Just like for cloud workloads, the future of AI within the enterprise will be hybrid. It cannot be otherwise.
The ASUS Turbo Radeon AI Pro R9700 is engineered to be the bedrock of this hybrid future. ASUS’s commitment to hardware reliability translates directly into enterprise-grade longevity.
Features like dual ball bearing, GPU Tweak III, a diecast shroud, phase-changing thermal paste and Asus GPU guard help the Radeon AI Pro R9700 perform better, for longer.
For more information on ASUS and its AI supporting hardware, you can visit the website.