HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support

2026-05-19 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

HPC-LLM is a retrieval-augmented and domain-adapted language model designed to provide operational support for High-Performance Computing (HPC) environments. It addresses the complexity researchers face with cluster environments, job schedulers, GPU resources, and parallel computing frameworks. The system integrates automated documentation ingestion from publicly available university HPC documentation, dense retrieval, and lightweight domain adaptation using QLoRA fine-tuning on Llama 3.1 8B. An HPC-oriented dataset of 9,000–24,000 training examples was constructed from crawled documentation, synthetic Q&A pairs, and curated expert knowledge. Benchmarking on JetStream2 infrastructure shows the adapted 8B model achieves performance comparable to the larger Qwen 2.5 14B model, while requiring significantly less GPU memory (5 GB) and offering faster inference. The framework is open-source and designed for local, resource-constrained deployments.

Key takeaway

For AI Engineers building specialized assistants for technical domains, HPC-LLM demonstrates that lightweight QLoRA fine-tuning combined with Retrieval-Augmented Generation (RAG) can achieve competitive performance against larger, general-purpose models. You should consider this approach to develop deployable, resource-efficient solutions for specific operational contexts, especially where privacy or infrastructure constraints limit cloud-based LLM usage. Evaluate the trade-offs between model size, domain adaptation, and hardware requirements for your target environment.

Key insights

Domain adaptation and RAG enable smaller LLMs to match larger models in specialized HPC support with fewer resources.

Principles

• Combine RAG with fine-tuning for domain-specific accuracy.
• Lightweight adaptation compensates for model scale in niche domains.
• Local deployment is feasible for resource-constrained environments.

Method

HPC-LLM uses automated documentation crawling, vector-based retrieval, QLoRA fine-tuning of Llama 3.1 8B on a custom HPC dataset, and local GPU inference within a modular orchestration pipeline.

In practice

• Fine-tune Llama 3.1 8B with QLoRA for HPC tasks.
• Use BGE-large-en-v1.5 for embedding queries in ChromaDB.
• Deploy on RTX 3090 or A100 40 GB with 4-bit NF4 quantization.

Topics

• High-Performance Computing
• Retrieval-Augmented Generation
• Domain Adaptation
• QLoRA Fine-tuning
• LLM Deployment

Code references

• huggingface/trl

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.