Instances for ML
Last updated on 2025-03-10 | Edit this page
The below table provides general recommendations for selecting AWS instances based on dataset size, computational needs, and cost considerations.
Genearl Notes:
- Minimum RAM should be at least 1.5X dataset size unless using batch processing (common in deep learning).
- The m5 and c5 instances are optimized for CPU-heavy tasks, such as preprocessing, feature engineering, and model training without GPUs.
- GPU choices depend on the task (T4 for cost-effective DL, V100/A100 for high performance).
- The g4dn instances are cost-effective GPU options, suitable for moderate-scale deep learning tasks.
- The p3 instances offer high-performance GPU processing, best suited for large deep learning models requiring fast training times.
-
Free Tier Eligibility: Some smaller instance types,
such as
ml.t3.medium
, may be eligible for the AWS Free Tier, which provides limited hours of usage per month. Free Tier eligibility can vary, so check AWS Free Tier details before launching instances to avoid unexpected costs.
Dataset Size | Recommended Instance Type | vCPU | Memory (GiB) | GPU | Price per Hour (USD) | Suitable Tasks |
---|---|---|---|---|---|---|
< 1GB | ml.t3.medium |
2 | 4 | None | $0.04 | Preprocessing, lightweight model training |
< 1GB | ml.m5.large |
2 | 8 | None | $0.10 | Preprocessing, regression, feature engineering, small model training |
< 1GB |
g4dn.xlarge (T4 GPU) |
4 | 16 | 1x NVIDIA T4 | $0.75 | GPU processing for small-scale deep learning, cost-effective GPU option |
< 1GB |
p3.2xlarge (V100 GPU) |
8 | 61 | 1x NVIDIA V100 | $3.83 | High-performance GPU processing, faster training for deep learning
models, higher cost but faster than g4dn
|
10GB | ml.c5.2xlarge |
8 | 16 | None | $0.34 | CPU-heavy processing, model training |
10GB | ml.m5.2xlarge |
8 | 32 | None | $0.38 | Preprocessing, feature engineering, model training |
10GB |
g4dn.2xlarge (T4 GPU) |
8 | 32 | 1x NVIDIA T4 | $0.94 | Moderate-scale deep learning, cost-effective GPU training |
10GB |
p3.2xlarge (V100 GPU) |
8 | 61 | 1x NVIDIA V100 | $3.83 | Faster GPU processing for deep learning, better suited for larger models if budget allows |
50GB | ml.c5.4xlarge |
16 | 64 | None | $0.77 | CPU-heavy processing, large model training |
50GB | ml.m5.4xlarge |
16 | 64 | None | $0.77 | Preprocessing, feature engineering, large model training |
50GB |
g4dn.4xlarge (T4 GPU) |
16 | 64 | 1x NVIDIA T4 | $1.48 | Moderate-scale deep learning, balanced performance and cost |
100GB |
g4dn.8xlarge (T4 GPU) |
32 | 128 | 1x NVIDIA T4 | $2.76 | Large-scale model training with cost-effective GPU acceleration |
100GB |
p3.8xlarge (V100 GPU) |
32 | 244 | 4x NVIDIA V100 | $15.20 | High-performance GPU processing for large deep learning models (e.g., transformers, CNNs) |
100GB |
p4d.24xlarge (A100 GPU) |
96 | 1,152 | 8x NVIDIA A100 | $32.77 | High-performance DL for large datasets with batch streaming |
1TB+ |
p3.16xlarge (V100 GPU) |
64 | 488 | 8x NVIDIA V100 | $30.40 | Extreme-scale deep learning, large transformer training |
1TB+ |
p4d.24xlarge (A100 GPU) |
96 | 1,152 | 8x NVIDIA A100 | $32.77 | Deep learning with batch processing for 1TB+ datasets |