GPU
A dedicated NVIDIA graphics card (GPU) is strongly recommended for training and applying deep learning models. Model training and applying performance on a GPU can be up to 20x faster than on a CPU, and can shorten training time from weeks to hours.
Since applying often takes seconds, compared to hours for training, a CPU can more reasonably be used if necessary. Please refer to the general System Requirements for more information on CPU requirements and recommendations.
Recommendations
Below is a table of recommended GPU options.
Specs
- Architectures: (later is better): Ada Lovelace > Ampere > Turing > Volta > Pascal
- RAM: The more the better. More important than core count. HBM2 > GDDR6X > GDDR6 > GDDR5X > GDDR5 in terms of memory bandwidth
- Cores: For a given architecture, more often leads to faster training and application times
Use Cases
- Deep Learning: Training and running deep learning models (requires 8+ GB RAM)
- Spotlight: Using Spotlight and Snap tools (requires 12+ GB RAM)
Model | Architecture | RAM | Cores | Deep Learning | Spotlight |
---|---|---|---|---|---|
NVIDIA RTX 6000 Ada | Ada Lovelace | 48 GB GDDR6 | 18176 | ✅✅✅ | ✅✅✅ |
GeForce RTX 4090 | Ada Lovelace | 24 GB GDDR6X | 16384 | ✅✅✅ | ✅✅✅ |
NVIDIA RTX 4500 Ada | Ada Lovelace | 24 GB GDDR6 | 7680 | ✅✅✅ | ✅✅✅ |
NVIDIA RTX 4000 Ada | Ada Lovelace | 20 GB GDDR6 | 6144 | ✅✅❌ | ✅✅❌ |
GeForce RTX 4080 Super | Ada Lovelace | 16 GB GDDR6X | 10240 | ✅✅❌ | ✅✅❌ |
GeForce RTX 4080 | Ada Lovelace | 16 GB GDDR6X | 9728 | ✅✅❌ | ✅✅❌ |
NVIDIA RTX 5000 Ada | Ada Lovelace | 16 GB GDDR6 | 9728 | ✅✅❌ | ✅✅❌ |
GeForce RTX 4070 Ti Super | Ada Lovelace | 12 GB GDDR6X | 8448 | ✅✅❌ | ✅❌❌ |
GeForce RTX 4070 Ti | Ada Lovelace | 12 GB GDDR6X | 7680 | ✅✅❌ | ✅❌❌ |
GeForce RTX 4070 Super | Ada Lovelace | 12 GB GDDR6X | 7168 | ✅✅❌ | ✅❌❌ |
GeForce RTX 4070 | Ada Lovelace | 12 GB GDDR6X | 5888 | ✅✅❌ | ✅❌❌ |
GeForce RTX 4060 Ti | Ada Lovelace | 8 GB GDDR6 | 4352 | ✅❌❌ | ❌❌❌ |
GeForce RTX 4060 | Ada Lovelace | 8 GB GDDR6 | 3072 | ✅❌❌ | ❌❌❌ |
NVIDIA RTX 2000 Ada | Ada Lovelace | 8 GB GDDR6 | 3072 | ✅❌❌ | ❌❌❌ |
NVIDIA RTX A4500 | Ampere | 20 GB GDDR6 | 7168 | ✅✅❌ | ✅✅❌ |
NVIDIA RTX A4000 | Ampere | 16 GB GDDR6 | 6144 | ✅✅❌ | ✅✅❌ |
GeForce RTX 3080 Ti | Ampere | 12 GB GDDR6X | 10240 | ✅✅❌ | ✅❌❌ |
GeForce RTX 3080 | Ampere | 10 GB GDDR6X | 8704 | ✅❌❌ | ❌❌❌ |
GeForce RTX 3070 | Ampere | 8 GB GDDR6 | 5888 | ✅❌❌ | ❌❌❌ |
Quadro RTX 6000 | Turing | 24 GB GDDR6 | 4608 | ✅✅✅ | ✅✅✅ |
Titan RTX | Turing | 24 GB GDDR6 | 4608 | ✅✅✅ | ✅✅✅ |
Quadro RTX 5000 | Turing | 16 GB GDDR6 | 3072 | ✅✅❌ | ✅✅❌ |
Quadro RTX 4000 | Turing | 8 GB GDDR6 | 2304 | ✅❌❌ | ❌❌❌ |
Tesla V100 | Volta | 16/32 GB HBM2 | 5120 | ✅✅✅ | ✅✅✅ |
Titan V | Volta | 12 GB HBM2 | 5120 | ✅❌❌ | ✅❌❌ |
Titan Xp | Pascal | 12 GB GDDR5X | 3840 | ✅❌❌ | ✅❌❌ |
Quadro P4000 | Pascal | 8 GB GDDR5 | 1792 | ✅❌❌ | ❌❌❌ |
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