Model deployment & versioning
Pin model artefacts, runtime settings and quantisation choices to a customer-specific release.
A controlled inference path, from application to silicon.
The technology layer is designed to make model deployment repeatable, measurable and portable across dedicated infrastructure configurations.
01Customer application
02Private authenticated API
03Customer-specific inference environment
04Model runtime
05Dedicated GPU node
06Monitoring and audit layer
Operations
Operational layers
Private networking & authentication
Restrict routes, authenticate requests and support future private connectivity patterns.
Storage & logging controls
Define model storage, request logging and retention behaviour according to the deployment policy.
Monitoring
Measure availability, latency, throughput, memory use and capacity without exposing prompt content by default.
Removal & reprovisioning
Document teardown, data removal and clean environment reprovisioning procedures.
Future portability
Develop repeatable deployment profiles that can move between compatible named European data centres.
Open source, operated with control
Built on proven open-source components, differentiated by orchestration, optimisation and operational control.
Depending on workload fit, the runtime may use independent open-source projects such as llama.cpp, vLLM or SGLang, with standard OpenAI-compatible API formats. These projects are not owned by INFERENC.
llama.cppvLLMSGLangOpenAI-compatible API
Validated today
✓ Local private API
✓ Multi-GPU large-model execution
✓ Locally controlled runtime
✓ No external model provider dependency
Research and development roadmap
→ Automated configuration selection
→ Production-grade tenant lifecycle
→ Portable deployment profiles
→ Audit-ready operational evidence