S&S Technologies Logo

sus-tech

<- Back To Blog

LLM Finetuning: LoRA and QLoRA

Alexander Schnabl
Alexander Schnabl ·

Fine-tuning large language models (LLMs) has become essential for organizations needing precise output in areas like legal, finance, or internal support. But how can you achieve high-quality customization without burning through GPU hours or engineering budgets?

The Problem

Full-parameter fine-tuning of LLMs sounds powerful - until you try it. Updating every weight in a 7B or 13B model means adjusting billions of values per training epoch. GPU memory needs spiral out of control, experimentation slows to a crawl, and even minor improvements start to feel costly.

For organisations in regulated or knowledge-heavy industries, delays in customizing models mean:

  • Loss of competitive edge in automation and AI-readiness.
  • Inability to comply with internal or industry-specific regulations.
  • More pressure on internal IT teams to manage manual, error-prone processes.

As outlined in our RAG vs. Finetuning: Choosing the Right Strategy for your LLMs, Retrieval-Augmented Generation addresses some needs. But when your use case demands that the model knows your domain natively - not just when prompted - then fine-tuning becomes essential.

Our Solution

At S&S Technologies, we offer pragmatic, cost-efficient fine-tuning of LLMs using LoRA (Low-Rank Adaptation) and its next-generation variant, QLoRA. These methods drastically reduce the compute required to adapt a foundation model to your data - be it legal terminology, financial document patterns, or internal support FAQs.

Whether you're building an internal knowledge bot, an AI-powered compliance checker, or a language-aware ERP assistant, our team fine-tunes LLMs to understand and speak your business language. Unlike traditional approaches, our implementations are:

  • Cost-optimized with GPU-efficient tuning
  • Fully integrated into platforms like n8n
  • Aligned to your ITSM, ERP, or document governance requirements

We combine tuned models with process-proven infrastructure like ITSM plugins, RAG pipelines, and Git-based workflow governance.

How It Works

Here's how LoRA and QLoRA make it all possible:

LoRA (Low-Rank Adaptation)

LoRA targets efficiency by modifying just a slice of the weight-space:

  1. Small change tracking - It preserves the pretrained model and trains only small, low-rank matrices to capture the adapted behavior.
  2. Memory savings - A 7B model can be fine-tuned by updating only ~86 million parameters at rank 512, rather than all 7 billion.
  3. Adjustable rank - Depending on the complexity of your domain, we calibrate LoRA's rank from 8-256. Higher ranks improve nuance; lower ranks train faster.

QLoRA (Quantized LoRA)

QLoRA builds on LoRA by reducing precision during training:

  • 4-bit quantization shrinks model memory overhead dramatically.
  • No loss in quality - Precision is restored during inference, retaining LoRA's accuracy while improving training throughput.
  • Full-layer adaptation - Applying QLoRA adapters to every transformer layer brings quality close to full fine-tuning.

Platform Integration

We embed fine-tuned models into existing business workflows:

  • Integrated with n8n flows that watch S3 buckets, ERP exports, or compliance docs.
  • Connected to ITSM UIs and ERP help panels.
  • Governed with role-based access and audit trails - just like in our Internal Knowledge Bases with RAG.

Business Impact

Clients adopting LoRA and QLoRA with us report:

  • Up to 70% reduction in compute and cloud costs for adaptation.
  • Faster deployment cycles, thanks to smaller, modular parameter changes.
  • Improved domain specificity, enabling models to reply with context-aware precision.
  • Compliance assurance - Fine-tuned models generate less hallucination and more governed responses when paired with internal data.

When combined with internal data pipelines, businesses can automate support or onboarding tasks at scale - just like clients using our n8n-based RAG pipelines.

Practical Next Steps

Ready to fine-tune a model for your domain using LoRA or QLoRA? Here's how we typically proceed:

  1. Discovery call - Clarify your goals, data environment, and candidate workflows.
  2. Dataset prep - Securely gather and structure your domain content.
  3. Model selection and tuning - Choose a base model and apply LoRA or QLoRA adapters.
  4. Workflow integration - Insert the tuned model into your n8n, ERP or ITSM systems.
  5. Govern and monitor - Track performance and maintain update flexibility.

Contact our team at office@sus-tech.at to explore how S&S Technologies can customize an LLM for your use case - and deliver results using efficient, governed automation. Automate. Optimize. Scale.

Tags: workflow automation, n8n, AI agents, ITSM automation, governed automation

S&S Technologies GmbH • UID Nr: ATU 77676212 • FN 571385y (LG Salzburg)
Haspingerstraße 4, 5550 Radstadt, Salzburg, Austria