Unlock the potential of LLMs with purpose-built GPU infrastructure
Gradient GPU Droplets handle your entire LLM development pipeline, from prototyping with ChatGPT APIs to fine-tuning Llama for your use case.
Stop letting memory constraints limit your LLM innovations
Gradient GPU Droplets provide specialized access to specialized hardware for LM inference, fine-tuning, and AI application development. With NVIDIA RTX series GPUs featuring tensor cores and optimized memory architectures, your team can efficiently implement LoRA (Low-Rank Adaptation), QLoRA (Quantized LoRA), and PEFT (Parameter Efficient Fine-Tuning) techniques for cost-effective model customization. Power AI applications from customer service bots to document analysis tools with optimized GPU performance.
GPU requirements for working with LLMs
Modern LLMs need powerful GPUs to run efficiently, from loading multi-billion parameter models into memory to generating real-time responses. Gradient GPU Droplets provide this with NVIDIA H100s (80GB), Mi325X GPUs, and AMD MI300X GPUs (192GB) designed for large-scale AI workloads.
Memory specifications for large language models
Large language models require substantial VRAM to store model parameters, optimizer states, and activation gradients during training. Attention mechanisms demand high-throughput data access, while gradient computation requires sustained memory performance. LLM fine-tuning GPU configurations must balance memory capacity with cost-effectiveness, particularly when implementing parameter-efficient methods like LoRA and QLoRA.
Architecture selection for transformer workloads
Gradient GPU Droplets offer NVIDIA RTX 4000 Ada Generation with enhanced tensor processing capabilities for versatile LLM fine-tuning workflows, NVIDIA RTX 6000 Ada Generation providing expanded memory capacity for larger models, and NVIDIA L40S delivering optimized performance for both training and inference tasks. These GPUs for large language model configurations provide excellent price-performance ratios for fine-tuning with GPUs, starting with cost-effective single-GPU setups and scaling to multi-GPU setups for LLMs distributed training environments.
Development environment optimization
Access pre-configured AI/ML-ready images with PyTorch, Hugging Face Transformers, and specialized libraries for PEFT. The platform supports containerized deployments with CUDA optimizations specifically tuned for transformer architectures, enabling reproducible LLM development environments across different projects and team members.
Scalability and deployment flexibility
Scale from single-GPU prototyping to multi-GPU production deployments with seamless resource allocation. Dynamic scaling capabilities support varying workloads from experimental fine-tuning runs to production inference serving, while maintaining consistent performance across different GPU configurations and deployment patterns.
LLM tasks powered by GPUs
Accelerate complex language model operations like multi-turn conversations and text generation with GPU-optimized processing that enables real-time inference, large-scale training, and efficient fine-tuning. High-performance GPU architectures deliver the parallel computing power necessary for transformer models to handle massive datasets and complex linguistic patterns with enterprise-level speed and accuracy.
Parameter-efficient fine-tuning methods
Implement cutting-edge PEFT techniques, including LoRA for memory-efficient model customization and QLoRA for ultra-low memory fine-tuning. These methods enable fine-tuning transformer models while preserving base model knowledge and reducing computational requirements. Deep learning fine-tuning workflows benefit from GPU-accelerated training, which maintains model quality while optimizing resource utilization.
Inference and deployment optimization
Optimize large language model serving with dedicated GPU inference for LLM configurations. Real-time inference workloads require different memory access patterns from training, so efficient batch processing and memory management become critical for responsive LLM applications. Purpose-built LLM infrastructure supports both interactive chat applications and batch processing workflows.
Research and experimentation workflows
Accelerate AI research with flexible resource allocation for hyperparameter tuning and model architecture exploration. Support rapid prototyping of novel transformer variants, ablation studies, and experimental fine-tuning approaches. Cloud-based GPU infrastructure enables researchers to test hypotheses quickly without infrastructure management overhead.
Choose the proper GPU configuration for your LLM workflows
Single-GPU configurations NVIDIA RTX 4000 Ada Generation (20GB):
Ideal for parameter-efficient fine-tuning with LoRA and QLoRA. Cost-effective entry point for research and development with excellent PEFT performance and energy efficiency. Best suited for models up to 7B parameters with memory-efficient techniques.
NVIDIA RTX 6000 Ada Generation (48GB):
Balanced performance for medium-scale transformer fine-tuning and research projects. High memory capacity enables larger batch sizes and model variants while maintaining accessible pricing. Optimal for teams requiring substantial memory without enterprise-grade costs.
NVIDIA L40S (48GB):
Professional-grade performance combining training capabilities with inference optimization. Enhanced tensor processing delivers superior throughput for fine-tuning and deployment scenarios—the best choice for production workloads requiring versatile performance across the complete model lifecycle.
Multi-GPU setups:
Distributed training configurations enable training larger models and processing bigger datasets through data and model parallelism. Scale beyond single-GPU memory limitations with coordinated gradient synchronization and load balancing. This is essential for models exceeding 48GB memory requirements or teams needing faster training iterations.
NVIDIA H100/H200 (80GB/141GB):
Industry-leading performance for large-scale model training and inference with advanced Transformer Engine optimization. The H200 offers nearly double the memory capacity of H100, enabling training of larger models with expanded context windows and batch sizes. Essential for cutting-edge research and production deployments requiring maximum computational throughput and memory bandwidth.
AMD MI300X/MI325X (192GB/256GB):
Exceptional high-bandwidth memory capacity ideal for memory-intensive workloads and massive model training. The MI325X provides industry-leading memory per GPU, enabling training of huge models or processing extensive datasets without memory constraints—a competitive alternative to NVIDIA solutions with superior memory-to-compute ratios for specific use cases.
FAQs
What is the best GPU for LLMs?
The best GPU for LLM depends on your specific workflow and budget. For parameter-efficient fine-tuning with LoRA and QLoRA, NVIDIA RTX 4000 Ada Generation provides excellent performance at accessible pricing. RTX 6000 Ada Generation offers 48GB capacity for larger models requiring more memory. NVIDIA L40S delivers the highest training and inference workloads, making it ideal for production LLM applications.
How much GPU memory is needed for LLMs?
VRAM requirements vary by model size and training approach. Small language models under 1B parameters work with 8-16GB configurations, while models like GPT-3.5 scale typically require 20-48GB. Parameter-efficient methods like LoRA reduce memory needs significantly. The 7B model typically requires 28GB and can fine-tune on 20GB with LoRA.
Which GPU is better for LLM?
RTX 6000 Ada Generation excels for research and development with 48GB memory at competitive pricing, making it ideal for medium-scale fine-tuning projects. L40S provides superior performance with optimized tensor processing and dual-purpose capabilities for both training and inference. Choose RTX 6000 for cost-effective development workflows, or L40S for production environments requiring maximum performance and enterprise features.
Should LLM use CPU or GPU?
Due to the parallel processing capabilities required by transformer architectures, GPUs provide essential acceleration for LLM tasks. While CPUs can handle small inference tasks, GPUs deliver 10-100X performance improvements for training and fine-tuning workflows. Even modest GPU configurations outperform high-end CPU setups for transformer operations.