Accelerated AI advisory
Where accelerated compute pays off in your stack, and where it does not. We size the problem first and design the architecture around it, so you commit to GPU capacity you will actually use.
NVIDIA Partner Network: Solution Advisor
AI that earns its compute.
Tarmac is an NVIDIA Solution Advisor: we advise on accelerated computing and then build the systems that run on it. We take no margin on hardware, so the advice is engineering rather than a sales pitch, delivered by senior teams in your time zone.
What we deliver
From GPU architecture to inference in production
One senior team across the whole path: what to build, what to run it on, and how to keep it fast and affordable once real users arrive.
Inference in production
Model serving that holds up under real traffic: optimized inference, sensible batching, and latency budgets you can actually hit. The unglamorous engineering that decides whether an AI feature ships or stalls.
Model customization
Fine-tuning, distillation, and retrieval over your own data, with the evals that prove the customized model beats the one you started with. Measured improvements, not vibes.
GPU infrastructure & MLOps
Provisioning, scheduling, and observability for GPU workloads, in the cloud or on hardware you own, wired into CI/CD and held to the Tarmac 10 quality process.
Cost & utilization
GPU capacity is expensive and easy to waste. We right-size instances, raise utilization, and cut idle spend, the same discipline that has taken client cloud bills down by 50% or more.
The data foundation underneath
Accelerated compute is only as good as what you feed it. We build the governed pipelines and data platform under the models, often on Databricks, so training runs on something trustworthy.
Why Tarmac
Advice with no hardware margin attached
The Solution Advisor track exists for partners who consult and implement rather than resell. That shapes the incentives, and the incentives shape the advice.
- 01
Advisors who also build
We are a Solution Advisor, not a reseller. We take no margin on your hardware, so our answer to "how much GPU do you need" is engineering, not a sales target. Then we build the thing and stay accountable for it.
- 02
Senior teams only
Our engineers average 10 years of experience and work in your time zone. You get people who have shipped production AI before, accountable for every merge, not a pyramid of juniors learning on your budget.
- 03
Right-sized and flexible
A focused senior team that scales up or down with the work. 6-month contracts with 30-day cancellation, so you are never locked into capacity you stopped needing.
- 04
Referral-led track record
85% of our new work comes from referrals, and we hold a 5.0 rating across verified Clutch reviews. Clients keep coming back because the outcomes hold up.
Questions, answered
NVIDIA partnership FAQ
Is Tarmac an NVIDIA partner?
Yes. Tarmac is a member of the NVIDIA Partner Network as a Solution Advisor. That is the consulting track of the program: partners selected to provide consultation and expert advice to customers implementing NVIDIA-based solutions. We advise on the architecture and build the software around it, and we are measured by whether it runs in production.
What does a Solution Advisor actually do?
We assess where accelerated compute earns its keep, design the architecture, and then build and operate the AI systems that run on it. The distinction that matters to you is commercial: a Solution Advisor advises and implements rather than resells, so our recommendation on how much GPU capacity you need is not attached to a hardware sale.
Do you resell NVIDIA GPUs or hardware?
No. We are an advisory and engineering partner, not a reseller, and we take no margin on hardware. You buy compute through whichever channel suits you, cloud or on-premises, and our job is to make sure you buy the right amount of it and get real utilization out of it.
Can you run GPU workloads in our existing cloud?
Yes. Our DevOps and Cloud team runs workloads across AWS, Azure, and GCP, and on self-managed hardware where the economics favor it. In practice the choice is usually driven by utilization: sustained training loads and bursty inference have very different answers, and we will tell you which one you have.
How does this relate to your AI and data services?
AI and Applied ML is what we build. NVIDIA is often what it runs on. This page is the NVIDIA-specific view of that work, and it pairs with our Databricks partnership on the data side: Databricks for the governed foundation, accelerated compute for the models on top.
Weighing up accelerated compute?
Tell us what you are trying to ship. We will tell you honestly whether GPUs are the answer, and if they are, bring the senior team that makes them pay off.