Instances for ML

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	Max Model Size (Approx.)
< 1 GB	ml.t3.medium	2	4	None	$0.04	Lightweight preprocessing or small models	Up to 100 M params
< 1 GB	ml.m5.large	2	8	None	$0.10	Regression, feature engineering, small CNNs or tree ensembles	Up to 500 M params
< 1 GB	g4dn.xlarge (T4 GPU)	4	16	1 × T4 (16 GB VRAM)	$0.75	Cost-effective GPU training for compact DL models	Up to 3 B params
< 1 GB	p3.2xlarge (V100 GPU)	8	61	1 × V100 (16 GB VRAM)	$3.83	High-performance GPU jobs; fine-tuning or inference	Up to 7 B params
10 GB	ml.c5.2xlarge	8	16	None	$0.34	CPU-heavy processing, tabular/ensemble models	Up to 500 M params
10 GB	ml.m5.2xlarge	8	32	None	$0.38	Preprocessing, feature engineering, boosting or linear models	Up to 1 B params
10 GB	g4dn.2xlarge (T4 GPU)	8	32	1 × T4	$0.94	Moderate-scale DL; training/inference	Up to 3 B–4 B params
10 GB	p3.2xlarge (V100 GPU)	8	61	1 × V100	$3.83	Deep learning workloads	Up to 7 B params
50 GB	ml.c5.4xlarge	16	64	None	$0.77	Large-scale CPU modeling or preprocessing	Up to 1 B params
50 GB	ml.m5.4xlarge	16	64	None	$0.77	Feature engineering, classic ML on wide data	Up to 1 B params
50 GB	g4dn.4xlarge (T4 GPU)	16	64	1 × T4	$1.48	Moderate DL models; efficient for batch inference	Up to 4 B params
100 GB	g4dn.8xlarge (T4 GPU)	32	128	1 × T4	$2.76	Large-scale DL training	Up to 5 B params
100 GB	p3.8xlarge (V100 GPU)	32	244	4 × V100	$15.20	Multi-GPU training	Up to 30 B params
100 GB	p4d.24xlarge (A100 GPU)	96	1,152	8 × A100 (40 GB each)	$32.77	High-end DL or fine-tuning for very large models	Up to 70 B+ params
1 TB+	p3.16xlarge (V100 GPU)	64	488	8 × V100	$30.40	Extreme-scale transformer training with distributed data parallelism	Up to 65 B params
1 TB+	p4d.24xlarge (A100 GPU)	96	1,152	8 × A100	$32.77	Batch or distributed training for foundation-scale models	Up to 70 B+ params