Faster, cheaper inference on the hardware you have.
A model that works in a notebook is not a model that serves ten thousand requests an hour within budget. We engineer the serving layer: quantisation, distillation, speculative decoding, continuous batching, KV-cache management, routing between models, and the GPU plan that makes it affordable.
Zingaro AI provides inference engineering for language, speech and vision models: serving stack selection and tuning, quantisation and distillation, speculative decoding, batching and caching, model routing, GPU capacity planning and cost optimisation, on-premise or in the cloud.
You get
- A serving stack in production with load tests
- A cost and latency report against the baseline
- Routing and caching policies
- A capacity plan for the next year of growth
Built with
- Quantisation
- Speculative decoding
- Continuous batching
- Prefix and KV caching
- Model routing
Inference engineering and optimisation, end to end.
Serving stacks
The right engine for the model and the hardware, tuned for your traffic pattern rather than the benchmark’s.
Quantisation and distillation
Smaller, faster models that keep the accuracy your eval suite requires.
Speculative decoding and caching
Draft models, prefix caching and KV-cache management for lower latency at the same cost.
Batching and scheduling
Continuous batching, priority queues and back-pressure so peaks do not turn into outages.
Model routing
Small models for easy requests, large ones for hard ones, decided per request with the quality measured.
GPU planning
How many, which ones, where, and when to buy versus rent. With the numbers from your own load tests.
From one painful job to a system in production.
Models and agents handle the volume. People handle the edges. You always know which did what.
- 01
Discover.
One or two weeks with your team. The jobs listed, sized and ranked. A number on the first one.
- 02
Build the pilot.
Fixed scope, fixed fee, 4 to 6 weeks. Real output on your real data, measured against the number.
- 03
Ship it.
Into production, inside your boundary if the rules require, with evals gating every release.
- 04
Run it, or hand it over.
We operate it with people on the queue and a weekly report, or your team takes it with the runbooks.
Your data does not have to leave the building.
On your servers
Air-gapped where required.
We install on machines you own, inside your network. Where the rules demand it, the system runs with no outbound connection and updates are carried in by hand. Your team keeps the keys.
Data stays inside your network
Your private cloud
We deploy into your account.
We deploy into your own cloud account, in your region, under your access controls. The data stays in your account. We get the access you grant, and nothing more.
Data stays inside your account
Ours
Managed, fastest to start.
We run it on infrastructure we operate. The right choice when the data is allowed to leave and you want the pilot running this month.
Managed by us, on our terms
In banking, insurance and healthcare, the rules decide where the data sits. We built for that first.
How each option worksWhat comes back, and how we measure it.
Deliverables
- A serving stack in production with load tests
- A cost and latency report against the baseline
- Routing and caching policies
- A capacity plan for the next year of growth
Measured by
- Latency at the percentiles that matter
- Throughput per GPU
- Cost per million tokens or per request
- Accuracy held against the eval suite
Real figures come from your pilot. We do not publish invented ones.
What people ask about inference.
Do we need GPUs on-site?
Only if the data must stay there. Otherwise we compare on-prem, cloud and hybrid with your numbers and let the cost decide.
How much can inference cost drop?
It depends on the starting point, so we measure first. Quantisation, routing and caching usually compound.
Which serving engines?
The one that wins on your hardware for your model. We test rather than assume.
Bring us the inference job you keep postponing.
Twenty minutes is enough to say whether we can take it.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences