The full AI stack, running inside your building.
Tensward selects, adapts, tests and tunes open models for your task, then runs them on hardware you own.
Private by architecture. Nothing leaves your network.
Fixed cost. No per-token bill.
Yours to control. Every release needs your approval.
Your requirements in. Production AI out.
Compare candidate models on your task before committing to one.
Fine-tune on your data, only where it measurably helps.
Critical cases and regressions gate every release.
Search serving settings on your GPUs, confirmed against what you run today.
Versioned packages, a local endpoint, rollback in one command.
Optimize runs on real GPUs today. The other stages are being built with design partners.
Task: referral readiness. 400 held-out cases.
Adapting on 3,200 labelled examples
Release gate: 48 clinician-reviewed cases
NVIDIA L4. Qwen2.5-7B-Instruct-AWQ. vLLM.
Releases on your endpoint
serve start --package throughputliveadapted model, gate passedrollback targetcurrent setup, importedarchivedOne product. Three layers you'd otherwise buy from three vendors.
An app vendor, a model provider and an infrastructure team rarely test together. Tensward ships the three as one versioned package.
- ApplicationThe workflow your team uses
- ModelChosen and adapted for the task
- InferenceTuned for your hardware
Measured on a GPU, not promised on a slide.
More output tokens per second, confirmed on repeat runs. Same model, same GPU, same 160 chat, tool and RAG requests.
It found the bottleneck first.
Up to 29 requests were waiting in the queue while the KV cache sat around 6% used. So it raised concurrency, 8 to 32, in one step.
max_concurrent_requests 8 → 32Tool calls held up.
49 requests offered tools. All 49 called a tool that was offered, and 92% passed the schema's argument checks.
Qwen2.5-7B-Instruct-AWQ on one NVIDIA L4, vLLM, September 2026. One workload on one GPU. Your numbers will differ, which is why we measure on your hardware.
Solutions built on the engine.
Specialty care
Referral readiness and follow-up review for pain and specialty clinics. Evidence-linked flags, and the clinician decides.
Explore specialty care →Next
- Finance and insuranceexploring
- Legalexploring
- Government and defenseexploring
- Materials and pharma R&Dexploring
Start with the open-source CLI.
It measures how your LLM is served on your own machine, offers one change at a time, and keeps a change only if it is faster and the answers pass your check.
uv tool install tensward- first word, typical
- 19 s → 299 ms
- requests/s
- 0.142 → 0.233
- output tok/s
- 70.7 → 107
One run from the README: Qwen3.8-27B int4 on an NVIDIA L4 with vLLM 0.30.0. The change turns off image input and rewords 86 of 96 answers, so it was kept only after a person read them. Your numbers will differ.
$ tensward tryTrying --max-num-batched-tokens 512 --language-model-only …This takes about 20 min. before after change─────────────────────────────────────────────────────────Requests/s 0.142 0.233 +64%Output tok/s 70.7 107 +52%First word, typical 19 s 299 msFirst word, slowest 1 in 20 42.4 s 311 ms -99%Answers 10 of 96 identical First word, slowest 1 in 20: -99%: a real gain.Not kept: 86 of 96 answers are worded differently (none wrong). Next: tensward try --keep keep it tensward serve serve what's kept now
Real output, shortened. Multi-GPU tuning, the automatic loop and A100 and H100 calibration are Tensward Enterprise.
Ready-made AI solutions for data that can't leave the building.
Tell us about the use case and the hardware you have.