How to Choose a GPU
Choosing the right GPU configuration is a key factor for successful AI development. The aifare platform offers a wide range of GPU models. This guide will help you select the most suitable GPU for your project needs.
GPU Architecture Categories
There are many GPU models available on the aifare platform. We roughly categorize them by architecture as follows:
NVIDIA Pascal Architecture
Such as GTX 1080 Ti. These GPUs lack low-precision hardware acceleration but provide moderate single-precision computing power. Due to their affordability, they are suitable for training small models (e.g., Cifar10) or debugging model code.
NVIDIA Volta/Turing Architecture
Such as GTX 20 series, Tesla V100, etc. These GPUs are equipped with TensorCores for low-precision (int8/float16) computation acceleration, but their single-precision performance is not significantly improved over the previous generation. We recommend enabling mixed-precision training in deep learning frameworks to accelerate model computation. Compared to single-precision training, mixed-precision training can typically provide more than 2x speedup.
NVIDIA Ampere Architecture
Such as GTX 30 series, Tesla A40/A100, etc. These GPUs feature third-generation TensorCores. Compared to the previous generation, they support the TensorFloat32 format, which can directly accelerate single-precision training (enabled by default in PyTorch). However, we still recommend using float16 mixed-precision training for higher performance gains.
NVIDIA Ada Lovelace Architecture
Such as RTX 40 series, Tesla L40/L40S, etc. The latest generation, offering stronger AI computing power and larger memory, suitable for large-scale model training and inference.
NVIDIA Blackwell Architecture
Such as RTX 50 series. The newest generation, providing top-tier AI computing performance, suitable for ultra-large-scale model training.
Choosing the Number of GPUs
The number of GPUs depends on your training tasks. Generally, we recommend that a model's training should be completed within 24 hours, so you can iterate and improve the model daily. Here are some suggestions for multi-GPU selection:
- 1 GPU: Suitable for small dataset training tasks, such as Pascal VOC
- 2 GPUs: Same as single GPU, but you can run two sets of parameters at once or increase the batch size
- 4 GPUs: Suitable for medium-sized dataset training tasks, such as MS COCO
- 8 GPUs: Classic configuration! Suitable for various training tasks and convenient for reproducing paper results
- More GPUs: For training large-parameter models, large-scale hyperparameter tuning, or ultra-fast model training
GPU Model Overview
Consumer GPUs
| Model | Memory | FP16 (TFLOPS) | FP32 (TFLOPS) | CUDA Cores | Tensor Cores | Architecture | Memory Type |
|---|---|---|---|---|---|---|---|
| RTX 5090 | 32GB | 209.6 | 104.8 | 21760 | 680 (3352 AI TOPS) | Blackwell 2.0 | GDDR7 |
| RTX 5090D | 32GB | 209.6 | 104.8 | 21760 | 680 (2375 AI TOPS) | Blackwell 2.0 | GDDR7 |
| RTX 5080 | 16GB | 112.56 | 56.28 | 10752 | 336 (1801 AI TOPS) | Blackwell 2.0 | GDDR7 |
| RTX 5070 Ti | 16GB | 88.7 | 44.35 | 8960 | 280 (1406 AI TOPS) | Blackwell 2.0 | GDDR7 |
| RTX 5070 | 12GB | 61.68 | 30.84 | 6144 | 192 (988 AI TOPS) | Blackwell 2.0 | GDDR7 |
| RTX 4090 | 24GB | 165.16 | 82.58 | 16384 | 512 (1321 AI TOPS) | Ada Lovelace | GDDR6X |
| RTX 4090D | 24GB | 147.08 | 73.54 | 14592 | 456 (1177 AI TOPS) | Ada Lovelace | GDDR6X |
| RTX 4080 | 16GB | 97.48 | 48.74 | 9728 | 304 (780 AI TOPS) | Ada Lovelace | GDDR6X |
| RTX 4070 Ti | 12GB | 80.18 | 40.09 | 7680 | 240 (641 AI TOPS) | Ada Lovelace | GDDR6X |
| RTX 4070 | 12GB | 58.30 | 29.15 | 5888 | 184 (466 AI TOPS) | Ada Lovelace | GDDR6X |
| RTX 4060 Ti | 16GB | 44.12 | 22.06 | 4352 | 136 (353 AI TOPS) | Ada Lovelace | GDDR6 |
| RTX 4060 Ti | 8GB | 44.12 | 22.06 | 4352 | 136 (353 AI TOPS) | Ada Lovelace | GDDR6 |
| RTX 4060 | 8GB | 30.22 | 15.11 | 3072 | 96 (242 AI TOPS) | Ada Lovelace | GDDR6 |
| RTX 3090Ti | 24GB | 80.00 | 40.00 | 10752 | 336 (320 AI TOPS) | Ampere | GDDR6X |
| RTX 3090 | 24GB | 71.16 | 35.58 | 10496 | 328 (285 AI TOPS) | Ampere | GDDR6X |
| RTX 3080Ti | 12GB | 68.20 | 34.10 | 10240 | 320 | Ampere | GDDR6X |
| RTX 3080 | 12GB | 61.28 | 30.64 | 8960 | 280 | Ampere | GDDR6X |
| RTX 3080 | 10GB | 59.54 | 29.77 | 8704 | 272 | Ampere | GDDR6X |
| RTX 3070 Ti | 8GB | 43.50 | 21.75 | 6144 | 192 | Ampere | GDDR6X |
| RTX 3070 | 8GB | 40.62 | 20.31 | 5888 | 184 | Ampere | GDDR6 |
| RTX 3060 Ti | 8GB | 33.40 | 16.20 | 4864 | 152 | Ampere | GDDR6X |
| RTX 3060 Ti | 8GB | 33.40 | 16.20 | 4864 | 152 | Ampere | GDDR6 |
| RTX 3060 | 12GB | 25.48 | 12.74 | 3584 | 112 | Ampere | GDDR6 |
| RTX 3060 | 8GB | 25.48 | 12.74 | 3584 | 112 | Ampere | GDDR6 |
| RTX 2080 Ti | 11GB | 26.90 | 13.45 | 4352 | 544 | Turing | GDDR6 |
| GTX 1080 Ti | 11GB | 22.68 | 11.34 | 3584 | N/A | Pascal | GDDR5X |
Professional GPUs
| Model | Memory | FP16 (TFLOPS) | FP32 (TFLOPS) | FP64 (TFLOPS) | CUDA Cores | Tensor Cores | Architecture | Memory Type |
|---|---|---|---|---|---|---|---|---|
| NVIDIA RTX A6000 | 48GB | 77.42 | 38.71 | 1.209 | 10752 | 336 | Ampere | GDDR6 |
| NVIDIA RTX A5000 | 24GB | 55.54 | 27.77 | 0.867 | 10752 | 256 | Ampere | GDDR6 |
| NVIDIA RTX A4000 | 16GB | 38.34 | 19.17 | 0.599 | 6144 | 192 | Ampere | GDDR6 |
| Quadro RTX 8000 | 48GB | 32.62 | 16.31 | 0.509 | 4608 | 576 | Turing | GDDR6 |
| Quadro RTX 6000 | 24GB | 32.62 | 16.31 | 0.509 | 4608 | 576 | Turing | GDDR6 |
| Quadro RTX 5000 | 16GB | 22.30 | 11.15 | 0.348 | 3072 | 384 | Turing | GDDR6 |
Cloud & Data Center GPUs
Tesla NVIDIA A Series GPUs
| Model | Memory | FP16 (TFLOPS) | FP32 (TFLOPS) | FP64 (TFLOPS) | CUDA Cores | Tensor Cores | Architecture | Memory Type |
|---|---|---|---|---|---|---|---|---|
| NVIDIA A100 SXM4 | 80GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A100 SXM4 | 40GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A100 PCIe | 80GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A100 PCIe | 40GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A800 PCIe | 80GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A800 SXM4 | 80GB | 38.98 | 19.49 | 9.746 | 6912 | 432 | Ampere | HBM2e |
| NVIDIA A40 PCIe | 48GB | 74.84 | 37.42 | 1.169 | 10752 | 336 | Ampere | GDDR6 |
| NVIDIA A30 PCIe | 24GB | 20.64 | 10.32 | 0.322 | 3584 | 224 | Ampere | HBM2e |
| NVIDIA A10 PCIe | 24GB | 62.48 | 31.24 | 0.976 | 9216 | 288 | Ampere | GDDR6 |
Tesla NVIDIA V Series GPUs
| Model | Memory | FP16 (TFLOPS) | FP32 (TFLOPS) | FP64 (TFLOPS) | CUDA Cores | Tensor Cores | Architecture | Memory Type |
|---|---|---|---|---|---|---|---|---|
| Tesla V100 PCIe | 16GB | 28.26 | 14.13 | 7.066 | 5120 | 640 | Volta | HBM2 |
| Tesla V100 PCIe | 32GB | 28.26 | 14.13 | 7.066 | 5120 | 640 | Volta | HBM2 |
| Tesla V100 SXM2 | 16GB | 32.71 | 16.35 | 8.177 | 5120 | 640 | Volta | HBM2 |
| Tesla V100 SXM2 | 32GB | 31.33 | 15.67 | 7.834 | 5120 | 640 | Volta | HBM2 |
| Tesla V100 SXM3 | 32GB | 32.71 | 16.35 | 8.177 | 5120 | 640 | Volta | HBM2 |
| Tesla V100S PCIE | 32GB | 32.71 | 16.35 | 8.177 | 5120 | 640 | Volta | HBM2 |
Tesla NVIDIA L Series GPUs
| Model | Memory | FP16 (TFLOPS) | FP32 (TFLOPS) | FP64 | CUDA Cores | Tensor Cores | Architecture | Memory Type |
|---|---|---|---|---|---|---|---|---|
| NVIDIA L40 | 48GB | 90.52 | 90.52 | 1414(GFLOPS1:64) | 18176 | 568 | Ada Lovelace | GDDR6 |
| NVIDIA L40S | 48GB | 91.61 | 91.61 | 1431(GFLOPS1:64) | 18176 | 568 | Ada Lovelace | GDDR6 |
Recommended Configurations
Entry-Level Configuration
- GPU: RTX 4090 (24GB)
- Scenario: Learning, small projects, image processing
- Advantage: High cost performance, sufficient memory
Professional Configuration
- GPU: A100 (40GB/80GB)
- Scenario: Large model training, enterprise applications
- Advantage: Professional training card, large memory, stable performance
Top-Tier Configuration
- GPU: H100 (80GB) or RTX 5090 (32GB)
- Scenario: Ultra-large-scale model training, research projects
- Advantage: Top performance, latest architecture
Cost Optimization Suggestions
- Choose as Needed: Select GPUs based on actual project requirements
- Flexible Billing: Use pay-per-second billing to avoid resource waste
- Monthly Packages: For long-term projects, consider monthly packages
- Multi-GPU Parallelism: Choose the appropriate number of cards based on training scale