Best Cloud GPUs for Large Language Models (LLMs)

Large Language Models (LLMs) such as GPT, LLaMA, and Falcon require substantial computational resources, particularly GPUs, for training, fine-tuning, and inference.

Choosing the right cloud GPU depends on model size, workload type (training vs. inference), latency and throughput needs, and cost constraints. This guide explores the best cloud GPUs for LLMs in 2025, comparing features, providers, and use cases to help you make an informed choice.

1. Understanding GPU Requirements for LLMs

LLMs consist of billions of parameters and demand high-performance GPUs with the following characteristics:

High Memory Capacity: Crucial for loading large model weights and KV cache.
High Bandwidth: Reduces latency by speeding up access to model data.
High FLOPS (Floating Point Operations per Second): Speeds up tensor computations in attention and feed-forward layers.
Multi-GPU Scalability: Supports distributed training and inference for very large models.
Cost Efficiency: Optimizes performance while keeping hourly cloud usage costs in check.

Latency and throughput requirements vary depending on the model. Smaller models (≤7B parameters) prioritize cost and response time, while larger models demand more GPU memory and compute power.

2. Top Cloud GPUs for LLM Workloads in 2025

GPU Model	Best For	Key Features	Cloud Providers	Typical Pricing (On-demand)
NVIDIA H100	Training & serving large LLMs	Highest FLOPS, large memory, ideal for large-scale training	AWS, Google Cloud, Azure, Nebius, Vultr	$2.00–$2.30/hr
NVIDIA A100	Deep learning, fine-tuning	Strong FP16 & INT8, MIG support, scalable	AWS, Google Cloud, Azure, Runpod, Vultr	~$1.19/hr
NVIDIA L40 / L40S	HPC, AI inference	Enhanced bandwidth, cluster networking	Nebius, Vultr	Starting at $1.67/hr
NVIDIA L4	Real-time inference, video analytics	Low latency, tensor operations support	Google Cloud (select providers)	Varies
NVIDIA A30	Data analytics, small-scale LLMs	Efficient for TensorFlow, PyTorch	Major cloud platforms	Varies
NVIDIA T4	Lightweight AI models, streaming	Balanced cost and performance	AWS, Google Cloud, Azure	Varies
NVIDIA RTX 6000 / A10G	3D rendering, content creation	Real-time ray tracing, high frame rates	Select cloud providers	Varies

These GPUs support diverse use cases, from large-model training to real-time inference deployments.

3. Choosing the Right GPU Based on Model Size

Small to Medium LLMs (≤7B Parameters)

Recommended GPU: NVIDIA G2 VMs (A100-based) or NVIDIA L4.
Why: These offer optimal throughput per dollar and good latency.
Use Cases: Chatbots, lightweight inference, fine-tuning.
Example: Serving LLaMA 2 7B with G2 instances ensures cost-effective performance.

Large LLMs (70B+ Parameters)

Recommended GPU: NVIDIA A3 VMs with A100 or H100.
Why: More memory and compute power support higher throughput.
Use Cases: Large-scale inference, model training.
Example: Deploying LLaMA 2 70B using multi-GPU A3 instances improves cost-effectiveness and performance.

Cutting-edge Research-Grade LLMs

Recommended GPU: NVIDIA H100.
Why: Delivers unmatched performance for latest-gen models.
Use Cases: Enterprise AI, generative model training, R&D.
Availability: Offered by AWS, Google Cloud, Azure, Nebius, Vultr.

4. Cloud Providers Offering GPU Instances

A range of cloud platforms offer AI-ready GPU instances:

AWS: Broad GPU options (A100, H100), global reach, flexible pricing.
Google Cloud: L4, A100, H100 instances; Kubernetes-friendly.
Azure: Integrated A100 and H100 offerings with ML services.
Runpod: Affordable GPU rentals with support for A100/H100.
Nebius & Vultr: Competitive pricing on L40, A100, and L4 GPUs.
Liquid Web: Bare metal GPU servers pre-loaded with AI/ML stacks.

Platforms like Vast.ai also offer budget-friendly, community-shared GPU rentals ideal for developers and researchers.

5. Cost vs. Performance Considerations

Key factors when evaluating cloud GPUs:

Throughput per Dollar: G2 (A100-based) excels for small models; A3 for large ones.
Latency Requirements: Real-time use cases need GPUs with fast memory bandwidth.
Batch Size Impact: Larger batches increase throughput but require more memory.
Multi-GPU Scaling: Critical for large LLMs; requires high-speed interconnects.
Pre-configured Environments: Reduce setup time with AI-ready OS and libraries.

6. Use Cases for Cloud GPUs in LLMs

Model Training: Accelerate convergence for LLMs with H100 and A100.
Inference and Deployment: Real-time LLM apps like chatbots and virtual agents.
Data Analysis and Simulations: Handle large datasets efficiently.
Content Creation: AI-assisted editing, generation, and rendering.
Healthcare Imaging: Faster diagnostics through AI-powered tools.
AI Research: Test and deploy experimental models with top-tier hardware.

7. Trends and Future Outlook

Emerging trends in 2025 impacting LLM GPU usage:

Longer Context Windows: New models increase memory demands.
Multi-Modal Models: Require versatile GPUs for audio, video, and text inputs.
Cost Optimization Tools: Platforms like Runpod and Vast.ai reduce access costs.
Prompt Compression Techniques: Improve inference efficiency by minimizing GPU load.

Summary

Aspect	Recommendation
Top GPU for Training	NVIDIA H100 (AWS, GCP, Azure, Nebius, Vultr)
Best for Large Inference (70B+)	A3 VMs with A100 or H100
Best for ≤7B LLMs	G2 VMs (A100-based), NVIDIA L4
Affordable Rental Options	Runpod, Vast.ai
Best for Pre-Configured AI Environments	Liquid Web GPU bare metal with Ubuntu & ML stacks
Key Factors	Memory, bandwidth, FLOPS, cost, latency, batch size, multi-GPU compatibility

Choosing the right cloud GPU for your LLM tasks in 2025 means balancing performance, budget, and deployment needs. For cutting-edge models, NVIDIA H100 leads the pack.

For smaller deployments, G2 or L4 GPUs offer high value. With emerging platforms and smarter serving techniques, access to powerful GPUs is more flexible and affordable than ever.