Private Inference Appliances: Integrated Hardware/Software Delivery for Compliance Scenarios
A private inference appliance packages the validated stack (vLLM/LMCache + the FX storage tier) into out-of-the-box whole-machine delivery: data stays in-domain, the stack is auditable, and performance is backed by signed measurements (throughput +29–40%).
Who needs the appliance form
Government, finance, healthcare, defense and other data-sovereignty scenarios, plus mid-to-large enterprises that need private LLM capability without an AI-infra team. Their common asks: a clear compliance boundary, a controllable delivery schedule, and no desire to walk the software-stack minefield themselves.
Mingxin's appliance path (one landing of the software-development capability line): domestic storage + imported/domestic heterogeneous GPUs + the open-source inference stack already validated on ROCm/Ascend/MetaX (backed by R1–R9), with no proprietary black-box components — customers can audit layer by layer.
Certainty in performance and acceptance
An appliance does not mean compromised performance: KV tiering and load acceleration come pre-integrated, shipping with a measured baseline (480B basis: throughput +29–40%, TTFT −26–32%, R2/R3; loading 6.2–9.3×, R9).
Acceptance follows the gate methodology: delivery inspection → baseline reproduction (R1 basis ±10%) → business-workload joint test → 72-hour stability — each gate stops if missed. "Out of the box" must also survive verification.
FAQ
How are models selected and configured?
Open-source models (Qwen/DeepSeek series and others) are pre-installed and tuned to business needs; model-to-hardware matching is sized from HBM and concurrency profiles (the ROI calculator gives a first range).
What about model upgrades later?
The weight-pool architecture supports hot updates natively: new model into the pool, canary traffic shift, old version retained for rollback — with load acceleration (6.2–9.3×, R9) the upgrade window is minutes.
Can domestic-substitution requirements be met?
FX is a domestic vendor's product and GPUs can be domestic platforms (Ascend/MetaX already measured, R5/R9); a component-level domestic-content list is provided at the business stage.
Data sources (verifiable)
Related reading
- Inference Storage Planning for a Thousand-GPU AI Datacenter
- Model Loading and Training Storage Acceleration for Ascend 910B Clusters
- HBM Efficiency and KV Tiering Practice on MetaX GPUs
Joint test first, decisions second: gate-based acceptance with built-in stop-loss
The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.
This site presents business-cooperation information and constitutes neither an investment offer nor any promise of returns. Measured data come from signed / official test reports (see the Evidence Library); vendor specs, public sources and estimates are labeled as such.