Parameter-efficient fine-tuning (PEFT) adapts LLMs by training tiny modules—adapters, LoRA, prefix tuning, IA³—instead of all weights, slashing VRAM use and costs by 50–70% while keeping near full-tune accuracy. Fine-tune and deploy budget-friendly LLMs on Runpod using smaller GPUs without sacrificing speed.

Discover how the NVIDIA RTX A6000 GPU delivers enterprise-grade performance for AI, machine learning, and rendering—with 48GB of VRAM and Tensor Core acceleration—now available on-demand through Runpod’s scalable cloud infrastructure.

Reduce AI model size by 90%+ without sacrificing accuracy using advanced compression techniques on Runpod—combine quantization, pruning, and distillation on scalable GPU infrastructure to enable lightning-fast, cost-efficient deployment across edge, mobile, and cloud environments.

Fine-tune Microsoft’s Florence-2 on Runpod’s A100 GPUs to solve complex vision-language tasks—streamline multimodal workflows with Dockerized PyTorch environments, per-second billing, and scalable infrastructure for image captioning, VQA, and visual grounding.

Generate unlimited, privacy-compliant synthetic datasets on Runpod—train AI models faster and cheaper using GANs, VAEs, and simulation tools, with scalable GPU infrastructure that eliminates data scarcity, accelerates development, and meets regulatory standards.

Accelerate your computer vision workflows on Runpod with GPU-optimized pipelines—achieve real-time image and video processing using dynamic batching, TensorRT integration, and scalable containerized infrastructure for applications from autonomous systems to medical imaging.

Deploy adaptive reinforcement learning systems on Runpod to create intelligent applications that learn from real-world interaction—leverage scalable GPU infrastructure, safe exploration strategies, and continuous monitoring to build RL models that evolve with your business needs.

Accelerate model development with Neural Architecture Search on Runpod—automate architecture discovery using efficient NAS strategies, distributed GPU infrastructure, and flexible optimization pipelines to outperform manual model design and reduce development cycles.

Protect your AI models and infrastructure with enterprise-grade security on Runpod—deploy secure inference pipelines with access controls, encrypted model serving, and compliance-ready architecture to safeguard against IP theft, adversarial attacks, and data breaches.