// service
Sovereign Inference & GPU Economics
Production-grade LLM serving on your own NVIDIA GPUs, with a dated, benchmarked answer to "local or frontier?" per workload.
shape
6–10 weeks
NVIDIA · Red Hat (RHEL ecosystem) · Intel (iGPU/NPU edge cases)
outcome
You get a working on-prem serving stack (vLLM, llama.cpp, ik_llama) tuned for your hardware and data-residency constraints, not a slide deck. Every model choice is backed by a benchmark of local 30B-class models versus hosted frontier models on real operational tasks, plus token/cost telemetry so GPU spend is a managed number. This is the sovereign-AI layer for organizations that cannot ship data to a public API.
what you get
- Inference architecture and model-routing/policy design with residency and compliance guardrails
- Engine selection and tuning on your GPU fleet (single- and multi-card configurations)
- Local-vs-frontier benchmark on your representative tasks, with methodology and dated results
- Token/cost telemetry and budget alerting wired into your observability stack
evidence — public, checkable
- club-3090 — multi-engine serving recipes for RTX 3090/4090/5090 (vLLM, llama.cpp, ik_llama)
- agentic-ops-bench — local 30B-class on one RTX 3090 vs hosted frontier models, real ops tasks
- semantic-router — programmable mixture-of-models routing for heterogeneous inference
- gpu_rtx_3090 — eGPU operations
- Sovereign inference routing and policy delivered for a Gulf AI holding (anonymized capability category)
Sovereign competence is demonstrated with what is public: serving recipes that ship
(club-3090 carries working configs for Qwen3.6-27B/35B and Gemma4-26B/31B on one and
two cards), an honest benchmark method (agentic-ops-bench), model routing
(semantic-router), and cost telemetry (agent-observability-stack). Client-specific
sovereign programs are referenced only as anonymized capability categories — never as
names, topologies, or details.
pairs with
Agent-Fleet Platform — Memory, Routing, Observability
A governed multi-agent platform with shared memory, model routing, and full token/cost/trace telemetry — so fleets are operable, not just demoed.
Agentic Network Operations — Intent-to-Fabric
Plain-language network intent becomes verified, on-device configuration changes with a closed telemetry loop — and zero unverified success reports.
Agentic CloudOps & AIOps — Signal to Action
Your telemetry stops being a wall of alerts and starts closing loops — events correlated, remediated under guardrails, and verified resolved.