10 Hugging Face Model Types and Domains that are Perfect for Private AI Infrastructure

But when it comes to running these models at scale, companies face a serious challenge: how to ensure data privacy, performance, customization, and cost control — without being locked into expensive or restrictive hyperscaler APIs.

That’s where OpenMetal’s Hosted Private Cloud and Bare Metal GPU infrastructure comes in. For organizations building production-grade AI systems or handling sensitive data, OpenMetal offers an ideal deployment environment: secure, customizable, and cost-predictable.

Let’s explore 10 types of Hugging Face models where OpenMetal is a natural fit.

Domain: Biomedical NLP

Example Models: BioGPT, ClinicalBERT, PubMedBERT

Use Case

• Extracting insights from vast repositories of biomedical literature, clinical trial data, or proprietary lab reports.
• These models are trained specifically on medical texts like PubMed and clinical records.
• BioTech and Pharma companies use them to power research assistants, automate scientific summarization, or prioritize drug targets from literature.

Challenge

Medical research involves sensitive, regulated data (e.g., under HIPAA or GDPR), and cloud-based APIs present compliance and IP leakage risks.

Why OpenMetal

Host BioGPT and related models on dedicated GPU servers within a private cloud environment, ensuring your data stays compliant and under full control while enabling high-speed inference and model retraining.

Domain: Legal NLP

Example Models: LegalBERT, CaseLaw-BERT

Use Case

• Legal document review, contract summarization, litigation research, and compliance analysis.
• These domain-tuned transformers are trained on statutes, case law, and legal contracts, making them ideal for automating document-heavy tasks in law firms or corporate legal teams.

Challenge

Confidential legal content must be kept in-house — uploading contracts to external APIs can breach attorney–client privilege or regulatory mandates.

Why OpenMetal

Run LegalBERT securely on GPU-backed private infrastructure that’s isolated, and performance-optimized.

Domain: Large Language Models (LLMs)

Example Models: LLaMA 3, Mistral, Falcon, GPT-J

Use Case

• General-purpose assistants, chatbots, summarization, internal copilots.
• These foundational LLMs are the open-source backbone of many modern AI applications.
• Enterprises are increasingly fine-tuning or instruct-tuning them on proprietary datasets to create custom assistants or document Q&A systems.

Challenge

API-based models like OpenAI’s GPT can’t be fully controlled, can’t be tuned freely, and incur rising costs with usage.

Why OpenMetal

Run full LLMs on your own private infrastructure — with no usage limits, no token fees, and full transparency. Perfect for fine-tuning on internal knowledge bases or enabling fully private conversations across your organization.

GPU Severs & Clusters Catalog

Questions? Schedule a meeting or start a chat.

Domain: Embeddings and Semantic Search

Example Models: Sentence Transformers like MiniLM, MPNet, etc.

Use Case

• Creating vector search engines, recommender systems, or similarity-based lookup.
• Sentence transformers convert documents, queries, or phrases into embeddings that can be compared for relevance or similarity. They’re the core of intelligent search, chat-based retrieval, and question-answering systems.

Challenge

Creating embeddings requires sending text into a model — a potential data privacy issue, especially when indexing internal documentation or customer communications.

Why OpenMetal

Generate and store embeddings in a secure, GPU-backed private cloud, and combine with vector databases (e.g., Chroma or FAISS) for lightning-fast semantic search.

Domain: Generative Image Models

Example Models: Stable Diffusion, ControlNet, Kandinsky

Use Case

• Creating product mockups, concept art, ad creatives, or internal design assets.
• These diffusion models can turn text prompts into photorealistic images.
• Businesses use them for prototyping, marketing, and visual ideation.

Challenge

Using public APIs (like Stability AI or Midjourney) introduces IP concerns — your generated assets and prompts may be logged, stored, or even reused.

Why OpenMetal

Deploy image generation models in-house on high-performance GPU nodes, with full control over versioning, outputs, and resource allocation.

Domain: Speech-to-Text Transcription

Example Models: Whisper, MMS

Use Case

• Transcribing meetings, support calls, interviews, and podcasts.
• Whisper and MMS (Multilingual Massively Multilingual Speech) are powerful tools for generating transcriptions in dozens of languages with high accuracy.

Challenge

Many transcription use cases involve sensitive recordings — from internal strategy meetings to protected healthcare or legal interactions.

Why OpenMetal

Avoid sending audio files to third-party platforms. Deploy Whisper on your own OpenMetal GPU cloud and build fully private transcription pipelines.

GPU Servers & Clusters Catalog

Questions? Schedule a meeting or start a chat.

Domain: Speech Representation Learning

Example Models: Wav2Vec 2.0, HuBERT

Use Case

• Custom speech recognition, speaker verification, voice classification.
• These models learn deep representations of audio, making them ideal for building bespoke voice AI — especially where accents, dialects, or jargon are involved.

Challenge

Fine-tuning voice models is compute-intensive and not viable on shared cloud GPUs long-term.

Why OpenMetal

Use OpenMetal GPU clusters for affordable, persistent fine-tuning and serving of voice models with domain-specific adaptations.

Domain: Tabular and Document-Based Question Answering

Example Models: TAPAS, DeBERTa, T5

Use Case

• Interrogating spreadsheets, forms, reports, and structured PDFs.
• These models allow users to ask natural-language questions about structured data sources — like “What were the total Q4 expenses by department?” — without writing queries.

Challenge

Financial, HR, and compliance data stored in spreadsheets or reports is sensitive.

Why OpenMetal

Use TAPAS or DeBERTa privately to extract answers without exposing raw documents. Pair with document loaders and OCR tools in a fully private AI stack.

Domain: Object Detection & Visual Understanding

Example Models: YOLOv8, DETR, DINOv2

Use Case

• Manufacturing defect detection, drone image processing, medical scan analysis.
• These state-of-the-art vision models are used across industries to identify and track objects in photos and video streams.

Challenge

Raw images and video often contain trade secrets, personal data, or security-sensitive content.

Why OpenMetal

Deploy computer vision inference pipelines with low latency and high throughput — running entirely within your private OpenMetal GPU infrastructure.

Domain: Parameter-Efficient Fine-Tuning

Example Models: PEFT-Enabled Models (LoRA, QLoRA, AdapterFusion)

Use Case

• Customizing LLMs without massive GPU budgets.
• PEFT methods let teams adapt large models with a small number of trainable parameters — enabling cost-effective domain tuning.

Challenge

Public cloud GPU pricing adds up fast during training and experimentation.

Why OpenMetal

Get full access to the GPU horsepower you need — whether for LoRA adapters, QLoRA quantization, or fine-tuning with bitsandbytes — in a dedicated, budget-friendly environment.

GPU Servers & Clusters Catalog

Questions? Schedule a meeting or start a chat.

Final Thoughts: Private AI for the Open Model Era

The open-source model ecosystem is booming — but most of the value comes when companies take full control of how those models are run, tuned, and served.

By combining open models from Hugging Face with OpenMetal’s private cloud and GPU infrastructure, organizations can unlock the full potential of AI while maintaining sovereignty over data, cost, and architecture.

• Want to run BioGPT on your own infrastructure?
• Want to fine-tune LLaMA-3 with full compliance and repeatability?

You don’t need to rely on Big Tech to do it. OpenMetal gives you the stack to run some of the top Hugging Face models on your terms.

Note: The models listed above are to provide example of the different Hugging Face model types and domains that can be supported by OpenMetal infrastructure. Connect with us to discuss the specific model you are interested in placing on OpenMetal’s private infrastructure.