AI PRACTICE LEADER · STRATEGY · APPLIED AI
I’m an AI Practice Leader in managed IT. I help organizations evaluate where AI can be useful, build a practical adoption plan, and turn promising ideas into workflows people can use.
My focus is AI strategy, adoption, and governance, supported by experience in technology planning, Microsoft 365, cybersecurity, and infrastructure. I bring those perspectives together to help teams make informed choices about their tools, data, security, and day-to-day work.
DeepWakeLabs is where I share my independent AI testing and technical research. I evaluate local models and frontier AI tools, build practical workflows, and document what works, what needs more testing, and where the tradeoffs show up.
Watch the work on YouTube (opens in a new tab)WHAT I FOCUS ON
I help teams identify useful applications for AI, assess their readiness, and set priorities for adoption. The goal is a practical plan that connects the technology to business needs and gives people a clear place to start.
I explore how organizations can put AI to work while understanding their data, permissions, governance, and security requirements. A useful workflow needs a clear purpose and a way to judge whether it is helping.
In the lab, I compare model quality, retrieval, speed, and memory use. I publish the configurations and limitations with the results so you can decide what is relevant to your own hardware and workload.
Explore the benchmarksDeepWakeLabs reflects my own testing and perspective. The aim is simple: make complex technology easier to evaluate and put to use.
I want to know whether a model can do useful work on hardware I can actually run.
QuantBench helps me see which tasks it handles and where it falls apart. HomHaystack checks whether it can find and use details in a long context. I look at both, along with memory use and how long I’m waiting for an answer.
The setup matters. I keep the quant, thinking mode, context, cache, and benchmark edition with the result. An unfinished run stays unfinished. A projected fit on a smaller GPU stays a projection.
The scores give me a shortlist. Then I try the models on the work I actually need done.
See the test details and results
Start with the workload, then leave room for it.
The recommendations weigh QuantBench, HomHaystack, and the recorded memory use. Most quality testing was on my RTX 5090. Guidance for 12, 16, and 24 GB cards is a starting point to verify on your own hardware.
Read the model picks and their limits
Practical evaluations for people working with local AI, hardware, and cybersecurity.
I’m interested in product evaluations, sponsored content, and technical collaborations that are relevant to the work I cover here.
Email me or contact me on X with a brief overview of your product, the proposed scope, and timing. Sponsorships and loaned hardware will be disclosed, and my conclusions will remain independent.
