GPU Types | NVIDIA Brev Documentation

Complete catalog of available GPUs with specifications and recommended use cases.

High Performance

Best for large model training and inference workloads.

GPU	VRAM	Architecture	Best for
NVIDIA H200	141GB HBM3e	Hopper	Large LLM training, HPC, multi-GPU inference
NVIDIA H100	96GB HBM3	Hopper	Large LLM training, multi-GPU inference
NVIDIA A100 80GB	80GB HBM2e	Ampere	LLM fine-tuning, large batch training

Great balance of performance and cost for most workloads.

GPU	VRAM	Architecture	Best for
NVIDIA L40S	44GB GDDR6	Ada Lovelace	Inference, fine-tuning, rendering
NVIDIA L40	48GB GDDR6	Ada Lovelace	Inference, fine-tuning, rendering
NVIDIA L4	22GB GDDR6	Ada Lovelace	Inference, video processing
NVIDIA RTX 6000 Ada	48GB GDDR6	Ada Lovelace	Visualization, rendering, inference
NVIDIA RTX PRO Server 6000	96GB GDDR6	Ada Lovelace	Enterprise visualization, rendering
NVIDIA A10G	22GB GDDR6	Ampere	Inference, video encoding
NVIDIA A16	16GB GDDR6	Ampere	Virtual workstations, inference
NVIDIA A6000	48GB GDDR6	Ampere	Professional visualization, training
NVIDIA A5000	24GB GDDR6	Ampere	Professional visualization, inference
NVIDIA A4000	16GB GDDR6	Ampere	Development, visualization
NVIDIA RTX 4090	24GB GDDR6	Ada Lovelace	Development, fine-tuning, inference
NVIDIA RTX 5090	32GB GDDR6	Blackwell	Development, fine-tuning, inference

Cost-effective options for development and small-scale inference.

GPU	VRAM	Architecture	Best for
NVIDIA T4	16GB GDDR6	Turing	Development, small model inference
NVIDIA V100	32GB GDDR6	Volta	Training, inference
NVIDIA P4	8GB GDDR6	Pascal	Inference, transcoding

Last updated: 2026-03-16T08:33:07Z

Model size: Ensure VRAM exceeds model parameters (7B params ~ 14GB for fp16)
Training vs inference: Training needs more VRAM than inference
Batch size: Larger batches require more VRAM but improve throughput