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Model Selection Guide

Choose a model path based on data sensitivity, deployment boundary, workload, hardware, context length, budget, and governance. This guide recommends an architecture path first, then model families.

SovAIHub principle

Do not start with model hype. Start with the boundary: where the data can go, where the model can run, how updates are approved, and how outputs are audited.

Data class

Private contracts, employee data, business-sensitive data.

Deployment boundary

Your own servers, GPUs, storage, and network controls.

Workload type

Answer from internal documents with citations.

Available hardware

Good for 7B–14B models and many RAG workloads.

Context need

Most RAG apps and internal assistants.

Budget priority

Control, auditability, and data boundary are priority.

Governance need

Prompt, response, source, user, and model traceability.

Language need

Primary documents and users are English.

Why this path

Your data class requires a controlled boundary, and you have infrastructure to self-host inference.
Self-hosting gives stronger data control, model version control, private networking, and auditability.
For RAG, retrieval quality, chunking, reranking, citations, and answer evaluation matter as much as model choice.

Implementation steps

1Benchmark 2–3 candidate models against your real evaluation set.
2Add retrieval, reranking, prompt templates, citation enforcement, and hallucination tests if using RAG.
3Deploy the selected model behind an internal API endpoint.
4Track token volume, latency, GPU utilization, failure cases, and evaluation scores.

Cautions

Do not choose a 70B model before testing whether 7B–14B solves the workload.
Hardware sizing should include context length, concurrency, and KV cache memory, not only model size.

Reference matrix

Common model selection patterns

Use this matrix as a starting point. Final selection should be validated with your own dataset, latency target, governance requirements, and hardware budget.

Scenario

Model path

Runtime

Notes

Air-gapped or classified environment

Open-weight model on approved offline runtime

vLLM, Ollama, llama.cpp, OpenShift AI

Use signed artifacts, checksums, offline registry, and audit logs.

Sensitive RAG with GPU infrastructure

Llama 3.1/3.3 8B–70B, Mistral, Qwen, Mixtral-class models

vLLM on private GPU servers

Prioritize retrieval quality, citations, reranking, and evaluation.

Sensitive data without GPU infrastructure

Managed cloud model inside enterprise tenant

Azure OpenAI, AWS Bedrock, Vertex AI with private networking

Conditionally sovereign. Validate region, logging, retention, and contracts.

High-volume classification or extraction

Small LLM or non-LLM classifier

Ollama, vLLM, ONNX Runtime, scikit-learn, XGBoost

Do not use a large frontier model if a smaller model solves the task.

Factory, IoT, sensor, or edge deployment

TinyML, small vision model, anomaly model, compact local LLM

TensorFlow Lite, ONNX Runtime, llama.cpp, device SDK

Requires device, data, latency, and update-process assessment.

Code assistant or repo intelligence

Code-specialized open model or managed frontier model

vLLM, private cloud, or controlled managed service

Use repo indexing, access controls, and source-grounded answers.

When not to use an LLM

If the task is deterministic routing, exact matching, simple classification, or rules-based validation, start with simpler automation before using an LLM.

RAG before fine-tuning

For private knowledge, start with retrieval, citations, and evaluation. Fine-tune only when behavior, format, or domain language requires it.

Edge AI is a separate path

Device AI needs hardware profiling, compact models, optimized formats, controlled updates, and field testing. Treat it as an engineering assessment, not a model dropdown.

Need a model architecture review?

SovAIHub can help compare local LLMs, managed cloud models, RAG design, edge deployment paths, hardware sizing, and governance controls for your environment.

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