On-Prem AI Inference and Model Training Made Easy: Fast Setup, Simple to Use, and Fits Your Budget

By Brian Cox | Jan 29, 2026 | AI, All

Phison had a great presence at the AI Infrastructure Tech Field Day event in May, where we discussed the challenges of AI inference and model training and introduced our aiDAPTIV+ solution to attendees.

While you can watch each of our Phison leaders’ full sessions on demand, TechStrong TV recently created a “director’s highlights” video cut and presented it in a Tech Field Data Insider webinar. In this cut, you’ll get a look at the key points of the following talks, along with commentary and discussion from a panel of experts:

- - Affordable on-premises LLM training and inference with Phison aiDAPTIV, by Brian Cox, Phison Director of Solution and Product Marketing
  - GPU memory offload for LLM fine-tuning and inference with Phison aiDAPTIV, by Sebastien Jean, Phison CTO

In this webinar, you’ll learn how to unlock large language model training on local hardware, reduce AI infrastructure costs, and enable private, on-premises AI with zero code changes.

View the webinar recording>>

Frequently Asked Questions (FAQ) :

What was the focus of Phison’s participation at AI Infrastructure Tech Field Day?

Phison focused on practical challenges institutions face when deploying AI inference and model training on-premises. The sessions addressed GPU memory constraints, infrastructure cost barriers, and the complexity of running large language models locally. Phison introduced aiDAPTIV as a controller-level solution designed to simplify AI deployment while reducing dependency on high-cost GPU memory.

What is the TechStrong TV “director’s highlights” webinar?

TechStrong TV produced a curated highlights cut from Phison’s Tech Field Day sessions, presented as a Tech Field Day Insider webinar. This format distills the most relevant technical insights and includes expert panel commentary, making it easier for IT and research leaders to grasp the architectural implications without watching full-length sessions.

Who are the Phison speakers featured in the webinar?

The webinar highlights two Phison technical leaders:

Brian Cox, Director of Solution and Product Marketing, who covers affordable on-premises LLM training and inference.
Sebastien Jean, CTO, who explains GPU memory offload techniques for LLM fine-tuning and inference using aiDAPTIV.

Why is on-premises AI important for universities and research institutions?

On-premises AI enables institutions to maintain data sovereignty, meet compliance requirements, and protect sensitive research data. It also reduces long-term cloud costs and provides predictable performance for AI workloads used in research, teaching, and internal operations.

What are the main infrastructure challenges discussed in the webinar?

Key challenges include limited GPU memory capacity, escalating infrastructure costs, and the complexity of deploying and managing LLMs locally. These constraints often prevent institutions from scaling AI initiatives beyond pilot projects.

How does Phison aiDAPTIV enable affordable on-prem AI training and inference?

Phison aiDAPTIV extends GPU memory using high-performance NVMe storage at the controller level. This allows large models to run on existing hardware without requiring additional GPUs or specialized coding, significantly lowering the cost barrier for local AI deployment.

What does “GPU memory offload” mean in practical terms?

GPU memory offload allows AI workloads to transparently use NVMe storage when GPU memory is saturated. For researchers and IT teams, this means larger models can be trained or fine-tuned without redesigning pipelines or rewriting code.

Does aiDAPTIV require changes to existing AI frameworks or code?

No. aiDAPTIV operates at the system and storage layer, enabling AI workloads to scale without modifying model code or AI frameworks. This is especially valuable for academic teams using established research workflows.

How does this solution help control AI infrastructure budgets?

By reducing reliance on expensive high-capacity GPUs and enabling better utilization of existing hardware, aiDAPTIV lowers capital expenditure while extending system lifespan. This makes advanced AI workloads more accessible to budget-constrained institutions.

Why should higher education stakeholders watch this webinar?

The webinar provides a real-world blueprint for deploying private, on-premises AI at scale. It offers actionable insights into lowering costs, improving resource efficiency, and enabling secure AI research and experimentation without cloud lock-in.