***

title: GPU Types
description: >-
Complete catalog of available GPUs on NVIDIA Brev with specifications and
recommended use cases.
----------------------

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

{/* GPU_TABLE_START */}

## 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        |

## Mid-Range

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   |

## Entry Level

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             |

{/* GPU_TABLE_END */}

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

## Choosing a GPU

* **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