Case studies
How teams in finance, insurance, retail and manufacturing run AI on their own infrastructure.
6,000-user AI on one pooled cluster.
A regulated AI platform, compliant by design.
One platform behind 50+ AI use cases.
One platform, every model, full control.
Every model on infrastructure they fully control.
Read the storyModel serving, consolidated.
Model serving consolidated onto Xinference.
Read the storyFinancial services6,000-user AI on one pooled cluster.
Everbright Securities needed one AI platform to serve 6,000 users across trading, research and operations, without giving up control of sensitive market data. Xinference pools 72 GPUs into a single scheduled cluster, serving 360,000 requests a day from one private environment.
- Fixed GPU allocation, split by desk and team
- Every new model meant manual setup, from scratch
- Onboarding a new team took 5 days
- One pooled cluster: 72 GPUs shared across the firm
- Smart scheduler matches traffic to capacity in real time
- New teams onboard in under 2 hours
“Xinference pools heterogeneous GPU resources in our private cloud, significantly reducing infrastructure cost for 6,000+ users while turning the latest open-source models into core productivity.”
InsuranceA regulated AI platform, compliant by design.
AIA needed an AI platform that would pass a regulator’s audit as readily as an engineering review. Xinference replaced a self-managed vLLM and Kubernetes stack with a governed platform built for compliance from day one, now serving 500K+ requests a day.
- Self-managed vLLM and Kubernetes stack
- New model rollout took 2 to 3 weeks
- Manual patching and compliance sign-off
- Governed platform: audit logs, role-based access built in
- New model rollout in 1 to 2 days
- Higher GPU utilisation, no self-managed ops
Food & retailOne platform behind 50+ AI use cases.
Across KFC, Pizza Hut and Taco Bell, more than 50 AI use cases used to mean 50 different setups. Xinference now serves all of it: 20+ models and 1.52M requests a day, from one platform running across dual data centers.
- AI use cases spread across teams and tools
- GPU utilisation under 25%
- Every new use case meant a new setup
- One platform behind 50+ scenarios, 20+ models
- 65% GPU utilisation across dual data centers
- 80%+ faster onboarding for new use cases
ManufacturingOne platform, every model, full control.
Siemens’ AI infrastructure team moved model deployment off manual, in-house pipelines and onto Xinference, giving the team one platform to run every model on infrastructure they fully control.
“Xinference gives us one platform to run every model on infrastructure we fully control.”
ManufacturingModel serving, consolidated.
TFC OpticalComms migrated its inference workloads onto Xinference to get more out of the GPU clusters it already owned, without adding new operational overhead.
“We consolidated model serving onto Xinference and stopped babysitting the stack ourselves.”
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