vLLM-Powered Multi-Model System

AINNA NeuralOps LLM Model Hub

Open-Source LLM Model Orchestra

AINNA NeuralOps leverages multiple open-source LLM models through vLLM to power e-commerce operations, NeuralOps agents, audit systems, coding tasks, analytics, and detached system buildingโ€”all with zero external API dependency.

7 LLM Models
vLLM Engine
100% Local
0 Token Cost

Open-Source LLM Models

Each model serves a specific role in the AINNA NeuralOps ecosystem

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Qwen

Alibaba Cloud

Primary Function General Chat & Multilingual
Best Use Case Business assistant, customer support, multilingual operation
NeuralOps Role Primary conversational interface for e-commerce operations
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DeepSeek R1

DeepSeek AI

Primary Function Deep Reasoning & Analysis
Best Use Case Strategic planning, audit, compliance review, complex decision-making
NeuralOps Role Deep reasoning for audit and strategic analysis tasks
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GLM

Tsinghua AI Lab

Primary Function Coding & System Generation
Best Use Case PHP/MySQL code generation, system repair, automated development
NeuralOps Role Coding agent for detached system building and maintenance
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Active

Gemma

Google DeepMind

Primary Function Fast Classification & Tagging
Best Use Case Product classification, tagging, simple automation, quick decisions
NeuralOps Role Lightweight classification engine for inventory and listing
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Llama

Meta AI

Primary Function Long-Context Analysis
Best Use Case Document analysis, knowledge review, comprehensive reporting
NeuralOps Role Long-context processor for inventory reports and knowledge bases
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Mistral

Mistral AI

Primary Function Translation & Rewriting
Best Use Case Translation, content rewriting, compliance-friendly content generation
NeuralOps Role Content transformation and localization specialist
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Kimi K2

Moonshot AI

Primary Function Long Context & Analysis
Best Use Case Long document analysis, research, multi-turn conversation
NeuralOps Role Long-context reasoning and research specialist
Model Evaluation

How Models Are Rated

Auto-cycling through NeuralOps LLM orchestra models โ€” each scored across 6 weighted dimensions

Currently Evaluating Qwen auto-cycling...
Accuracy 0%
Task Fit 0%
Speed 0%
Cost Efficiency 0%
Safety 0%
Low Edit Needed 0%
Performance Radar
NeuralOps LLM Rating
0 / 100

Intelligent Model Selection

Models are not directly connectedโ€”they are linked through Neural Router

The AINNA Neural Router analyzes each request and routes it to the most appropriate model based on task type, complexity, and expected output.

User Request
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Neural Router
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Selected LLM
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Process & Output
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Detached Output

Routing Infrastructure

Neural Router

Analyzes request intent and selects optimal model

API Gateway

OpenAI-compatible endpoint for unified model access

Task Classifier

Categorizes requests: audit, coding, analysis, summary

Shared Database

Centralized knowledge base for all models

Logging Layer

Complete request/response audit trail

Agent Workflow

Orchestrates multi-step tasks across models

Architecture Overview

End-to-end architecture from user request to detached system output

Interface Layer
Open WebUI / Admin Panel
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Routing Layer
AINNA Neural Router
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Inference Layer
vLLM Model Server
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Model Pool
Qwen
DeepSeek
GLM
Gemma
Llama
Mistral
Kimi K2
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Detached Systems
Inventory
Listing
Sales Report
Finance
Compliance
Audit
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Data Layer
AINNA 80K SKU / 30 Store Operations

Why Multi-Model Approach?

Each model is selected for specific task optimization

Lower Cost

Open-source models eliminate per-token API charges

Better Task Matching

Right model for right task improves accuracy

Faster Response

Local inference with optimized model selection

Reduced Dependency

No single-vendor lock-in or rate limits

Local Knowledge

All data stays within infrastructure

Better Audit

Clear separation of model responsibilities

Scalable

Foundation for SME client expansion

Fine-tunable

Custom training on proprietary data

Enterprise-Grade Protection

Data sovereignty and security at every layer

No Credential Exposure

Marketplace credentials never stored in plain text or exposed to models

Role-Based Access

Fine-grained permissions for admin, operator, auditor roles

API Key Vault

Encrypted storage for third-party service credentials

Request Logging

Complete audit trail of all model interactions

Output Audit Trail

Every model response logged and timestamped

Private Deployment

Optional fully isolated on-premise installation

Data Sovereignty Focus

AINNA NeuralOps is designed for Malaysian SMEs and enterprise clients who require complete control over their data and AI operations. All processing occurs within local infrastructure.

Planned Expansion

Building the next generation of SME NeuralOps infrastructure

1

Dedicated GPU Server

High-performance inference server for real-time processing

2

University / IPTA Collaboration

Partner with Malaysian institutions for model research

3

SME AI Service Layer

Multi-tenant SaaS platform for Malaysian businesses

4

OpenClaw Agent Network

100-node distributed NeuralOps agent infrastructure

5

Local Model Fine-tuning

Custom models trained on AINNA operational data

6

Multi-Agent Detached Builders

Automated system generation from specifications

Ready to Explore?

Learn more about the AINNA NeuralOps architecture and model routing

AINNA NeuralOps System