RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI

Researchers demonstrate that small language models (3-4B parameters) can achieve strong multi-task radiology performance through LoRA fine-tuning, enabling deployment on consumer-grade CPUs without GPUs. The RadLite system, trained on 162K samples across 9 radiology tasks, shows dramatic performance improvements over zero-shot baselines and can be quantized to 1.8-2.4GB for practical clinical deployment.

The RadLite research addresses a critical bottleneck in medical AI adoption: the computational barrier preventing deployment in resource-constrained clinical environments. While large language models show promise in radiology, their GPU requirements make them impractical for many healthcare facilities, particularly in underserved regions. This work demonstrates that architectural efficiency combined with task-specific fine-tuning can deliver comparable performance at a fraction of the computational cost.

The research builds on growing recognition that scale alone doesn't guarantee domain-specific performance. The 53-89% accuracy improvements through LoRA fine-tuning over zero-shot baselines underscore how specialized adaptation outperforms in-context learning for technical domains like radiology. The finding that few-shot prompting actually degrades fine-tuned model performance challenges conventional assumptions about LLM capabilities and suggests that quantitative fine-tuning provides deeper domain understanding than prompt-based approaches.

For healthcare practitioners, RadLite offers genuine accessibility advantages. Deployment on consumer CPUs (~$500-1000 hardware) eliminates infrastructure investments that have historically gatekept AI adoption in smaller clinics. The complementary strengths between Qwen2.5 and Qwen3 models suggest that ensemble approaches, even with modest computational overhead, can achieve robust multi-task performance without requiring larger foundational models.

The broader implication extends beyond radiology: this work validates that domain-specialized small models represent a viable category for enterprise deployment. As clinical AI regulations tighten around explainability and computational transparency, smaller interpretable models may gain regulatory advantages over black-box larger systems. Future development should focus on clinical validation through prospective studies and integration with existing clinical workflows.

• →Small 3-4B parameter models achieve 53-89% accuracy improvements in radiology tasks through LoRA fine-tuning compared to zero-shot baselines.
• →Models quantized to 1.8-2.4GB enable practical deployment on consumer CPUs at 4-8 tokens/second without GPU requirements.
• →Task-specific fine-tuning proves more effective than few-shot prompting for specialized domains, contradicting conventional LLM scaling assumptions.
• →Complementary model strengths suggest ensemble approaches can achieve hospital-grade multi-task radiology performance on commodity hardware.
• →Consumer-hardware deployment eliminates infrastructure barriers, potentially accelerating AI adoption in resource-constrained clinical environments.