Best GPUs for Deep Learning
Deep learning GPU selection depends on three factors: the model architecture, the dataset size, and whether the workload is training or inference. Convolutional neural networks (CNNs) for computer vision are typically less memory-intensive than transformer models for NLP, but both benefit from higher memory bandwidth and faster tensor core operations.
The most important specs for deep learning GPUs are VRAM capacity (determines the maximum model and batch size), memory bandwidth (determines how fast data moves between memory and compute units), and tensor core count (determines the throughput for matrix operations that dominate neural network computation). FP16 and BF16 performance is more relevant than FP32 for modern deep learning, as mixed-precision training is now the default approach.
Cloud GPU rental is the standard approach for deep learning work that exceeds what a local workstation can handle. The cost advantage over purchasing hardware is significant: an H100 costs $30,000-$40,000 to buy, while cloud rental starts at around $2/hr. For a research project that needs 100 GPU-hours, renting costs approximately $200 versus a $30,000+ capital expenditure.
Recommended GPUs
H100 SXM5
The H100 SXM is the top choice for deep learning training at scale. Its fourth-generation tensor cores and transformer engine deliver up to 3x the training throughput of the A100 on transformer-based models. NVLink 4.0 enables efficient multi-GPU communication for distributed training across 2, 4, or 8 GPUs. The 80GB of HBM3 memory accommodates large batch sizes and model states.
View all H100 SXM5 offers →A100 SXM4 80GB
The A100 80GB remains the workhorse of deep learning. It handles the full range of workloads from CNNs to transformers to diffusion models at a price point 40-60% lower than the H100. The mature software ecosystem means near-universal framework support. For teams that do not need the absolute fastest training times, the A100 offers the best balance of performance, compatibility, and cost.
View all A100 SXM4 80GB offers →H200 SXM
The H200 extends the H100 architecture with 141GB of HBM3e, nearly doubling the memory capacity. This is particularly valuable for deep learning workloads that are memory-bound: large batch training, models with large embedding tables, or research that requires keeping multiple model copies in memory for techniques like model averaging.
View all H200 SXM offers →B200 SXM
The B200 represents the current state of the art for deep learning hardware. Its Blackwell architecture delivers up to 2x the FP8 performance of the H100, and the 192GB of HBM3e eliminates memory as a bottleneck for most workloads. Availability is still limited and pricing is at a premium, but for organizations pushing the boundaries of model scale, the B200 is the clear choice.
View all B200 SXM offers →L40S
The L40S is a strong mid-tier option for deep learning. Its 48GB of GDDR6 memory and Ada Lovelace architecture make it suitable for training medium-sized models and for inference serving. It lacks the HBM memory type of the A100 and H100 (meaning lower memory bandwidth), but for workloads that are not memory-bandwidth-bound, the L40S delivers good performance at a lower cost.
View all L40S offers →RTX 4090
The RTX 4090 is the entry point for deep learning on the cloud. Its 24GB of GDDR6X and consumer-grade Ada Lovelace architecture handle small to medium models (up to approximately 500M parameters comfortably, larger with gradient accumulation). It is not designed for production training of large models, but for research prototyping, hyperparameter search, and small-scale experiments, it is the most cost-effective option available.
View all RTX 4090 offers →RTX A6000
The RTX A6000 offers 48GB of GDDR6 in a workstation-class form factor. It sits alongside the L40S in terms of memory capacity but uses the older Ampere architecture. The A6000 is widely available across providers and is a reliable choice for deep learning workloads that need more than 24GB but do not require the bandwidth of an A100.
View all RTX A6000 offers →MI300X
AMD's MI300X offers 192GB of HBM3 memory. For deep learning teams using PyTorch with ROCm support, it is a cost-effective alternative to the H100 and H200 for workloads that prioritize memory capacity. The ROCm ecosystem has improved significantly, but some CUDA-specific optimizations (FlashAttention, Triton kernels) may require adaptation.
View all MI300X offers →Live Pricing Comparison
Prices update every 60 seconds. Data from 28 cloud GPU providers tracked by GpuPerHour.
| GPU | VRAM | From | Cheapest On | In Stock | Best For |
|---|---|---|---|---|---|
| H100 SXM5 | 80GB | $1.47/hr | Vast.ai | 1 | Large-scale distributed training |
| A100 SXM4 80GB | 80GB | $0.73/hr | Vast.ai | 4 | General-purpose training, best value |
| H200 SXM | 141GB | $1.97/hr | Vast.ai | 2 | Large models, memory-intensive training |
| B200 SXM | 192GB | $2.67/hr | Vast.ai | 3 | Next-generation training, largest models |
| L40S | 48GB | $0.47/hr | TensorDock | 1 | Inference, moderate training, cost-effective |
| RTX 4090 | 24GB | $0.20/hr | Vast.ai | 0 | Experimentation, small models, prototyping |
| RTX A6000 | 48GB | $0.27/hr | ThunderCompute | 0 | Workstation workloads, moderate training |
| MI300X | 192GB | $0.95/hr | Crusoe | 6 | ROCm-compatible training, large memory |
Choosing by Workload
Computer vision workloads (CNNs, object detection, segmentation) are typically less memory-intensive than NLP workloads. A ResNet-50 trains comfortably on a single GPU with 16GB of VRAM. Larger vision models like ViT-Large benefit from 40-80GB. The A100 80GB is the sweet spot for most computer vision training: it offers enough memory for large batch sizes and its memory bandwidth handles the data-loading demands of image datasets.
NLP and transformer workloads are the most demanding category. Pretraining a large language model requires hundreds or thousands of GPU-hours on H100 or B200 class hardware. Fine-tuning is more accessible: a 7B parameter model can be fine-tuned on a single RTX 4090 using parameter-efficient methods. Inference serving for transformer models is memory-bound: the model must fit in VRAM, and batch size is limited by remaining memory after the model is loaded.
Generative models (Stable Diffusion, image generation, video synthesis) require 16-48GB of VRAM depending on the model size and resolution. The RTX 4090 handles Stable Diffusion XL at standard resolutions. Larger models like Flux and video generation models benefit from the A100 or L40S for their additional memory.
Reinforcement learning workloads have moderate GPU requirements compared to supervised learning. The compute is typically dominated by environment simulation (which may run on CPU) rather than neural network forward/backward passes. An RTX 4090 or A100 40GB is sufficient for most RL research.
Frequently Asked Questions
Which GPU is best for PyTorch deep learning?▾
All modern NVIDIA GPUs support PyTorch with CUDA. The best choice depends on the workload size. For experimentation and small models, the RTX 4090 offers the lowest cost. For production training, the A100 80GB is the most widely used. For maximum performance, the H100 SXM delivers up to 3x the training throughput of the A100. AMD GPUs (MI300X) also support PyTorch through ROCm.
How much VRAM do I need for deep learning?▾
For most deep learning experiments, 24GB (RTX 4090) is sufficient. Medium-scale training (models with 100M-1B parameters) benefits from 48-80GB (L40S or A100). Large-scale training (1B+ parameters, large batch sizes) requires 80GB+ (H100, H200, or B200). The general rule: if the model plus optimizer states plus a reasonable batch size does not fit in memory, the next GPU tier up is needed.
Is the RTX 4090 good for deep learning?▾
The RTX 4090 is excellent for deep learning experimentation and small-to-medium model training. Its 24GB of VRAM handles models up to approximately 500M parameters with standard batch sizes. Limitations: it lacks HBM memory (lower bandwidth than A100/H100), does not support NVLink (limiting multi-GPU scaling), and 24GB is insufficient for large transformer models.
H100 vs A100 for deep learning: which should I choose?▾
The H100 offers approximately 3x the training throughput of the A100 for transformer models and 2x for CNNs, with the same 80GB memory capacity. The A100 costs 40-60% less per hour. Choose the H100 when training time is the bottleneck (large models, tight deadlines). Choose the A100 when cost efficiency matters and slower training is acceptable.
How much does deep learning training cost per hour?▾
Cloud GPU costs for deep learning range from under $0.30/hr for an RTX 4090 (small experiments) to over $5/hr for an H100 SXM (production training). A typical research project might use 100-500 GPU-hours, costing $50-$2,500 depending on the GPU chosen.