Dedicated Inference
Matched to your workload. We tune a private endpoint around your real traffic, not a spec sheet, on TensorX-owned GPUs in the EU. Sovereign by design, with zero data retention on every request.
You are not calling our model. You are calling yours.
Enter your company name and watch a dedicated private endpoint provision on an NVIDIA B300 node in the EU. An illustration of what we stand up for you, end to end.
This is an interactive illustration. Your real endpoint is sized against your traffic during the engagement below. Book a call to scope your workload together.
Not another GPU-as-a-Service. We start with analysis and optimisation, from day one.
Most providers sell you a GPU and a price list. We start from your traffic and work backwards to the smallest, most resilient footprint that holds your latency targets. We are your inference engineering partner, not just a capacity vendor. We will tell you which model and configuration actually fits your workload, even when that means recommending less hardware. Everything runs on TensorX-owned GPUs in the EU, with zero data retention, and we never use or store your content, even while we are replaying your traffic.
We don't just rent GPUs. We tune them to you.
Most providers
GPU-as-a-Service. You carry the risk.
TensorX
Workload-matched. We carry the analysis.
From your traffic to a tuned deployment
A consultative engineering engagement, in seven phases. The evidence comes from your own workload, replayed on the hardware you would run on.
Discovery and traffic analysis
We ingest your real production call logs and build a precise profile of your workload: peak concurrency, throughput, the full latency picture, context-size distribution, cache locality, daily rhythm and the exact tokeniser. Privacy-safe throughout, from lengths and metadata, never prompt or completion content.
A clear, quantified picture of what your AI workload actually looks like.Team and requirements alignment
We work directly with your engineering and product teams to turn business needs into engineering requirements: the priority workloads, the latency and uptime you need to feel, compliance constraints, any incidents you have hit, and your growth trajectory.
A shared definition of "good", the SLAs the deployment must hold.Simulation on our clustersThe core
The core of the engagement. We replay your real traffic, token for token, on the exact B300 hardware you would be served on, scaled from current volume to several times peak. We measure what users feel, trial advanced serving strategies and keep only what helps your shape, and reproduce any incident to prove which configuration removes it.
Evidence, on your own workload, instead of vendor claims.Configuration scoping and model fit
From the data we derive the exact footprint and serving configuration that suits your workload, and the right model for each task, scored on output quality, not just speed. We size on a simple, honest metric: how many concurrent users a configuration carries while staying inside your latency SLA.
A recommended footprint, model guidance, and a tuned configuration proven against your traffic.Recommendation and options
We present a clear set of footprint options with honest pros and cons, flat and predictable monthly pricing, and our recommended pattern: a dedicated baseline that runs hot, with automatic burst onto shared capacity for spikes, so you never pay for idle hardware to chase a one-minute peak.
A decision you can make with confidence, and budget you can plan around.Onboarding, validation and migration
We stand up the dedicated instance, point your key at it, and validate sustained performance under your real concurrency together. Migration is progressive, headroom first and traffic second, with an optional short quality evaluation on your hardest tasks.
A safe, evidence-based migration onto a deployment proven for your traffic.Ongoing partnership
We keep current open-weight models production-ready fast, monitor the agreed SLAs, and re-tune as your workload evolves.
An inference partner that grows with you, not a capacity invoice.Evidence, sovereignty, partnership
Sized on your traffic, not a guess
We replay your real production calls on the same hardware you would run on.
Capacity that holds under load
We measure concurrent users within your latency SLA, not peak throughput.
We reproduce and remove your incidents
Proven on your workload, not promised in a deck.
Partner, not vendor
We recommend the right model per task, and less hardware when that is the right answer.
Sovereign by design
EU-owned GPUs, zero data retention, built for GDPR. Your content is never used or stored, even during analysis.
Latest models, fast
Current open-weight models, production-ready quickly after release.
What you walk away with
Let us scope and size your deployment together
Bring your traffic logs. We will profile your workload, replay it on a B300, and show you the footprint that holds your latency, with evidence rather than claims.
Book a call