In NVIDIA's GPU family, which models or series are good for training and running GPT-4o model

When considering NVIDIA GPUs for training or running large language models like GPT-4o, the right choice depends on the specific task:

• Training from scratch or fine-tuning large models (requires high-end, multi-GPU setups with very high VRAM and interconnect bandwidth).

• Running inference (using the model) — much less demanding and can be done on a single powerful GPU.

 ⚙️ For Training or Fine-Tuning GPT-4o-Level Models (Enterprise/Research)

These models are ideal for training large LLMs (100B+ parameters like GPT-4o):

A. Hopper Series (Latest, Best-in-Class)

• H100 (80 GB or SXM version with 94 GB HBM3)

  • Best for training large models.

  • Up to 3–4x faster than A100 in LLM training.

  • NVLink/NVSwitch for high-speed multi-GPU setups.

  • FP8 support for better efficiency in LLMs.

B. Ampere Series (Still Relevant)

• A100 (40 GB and 80 GB)

  • Very popular for training LLMs until recently.

  • Available in PCIe or SXM form.

  • High memory bandwidth, multi-GPU support with NVLink.

C. Blackwell Series (Coming in 2025)

• B100, B200

  • Even better than H100, tailored for next-gen AI.

  • Will become the new standard for massive-scale training (not generally available yet).

⚙️ For Inference / Smaller Scale Fine-Tuning

Ideal if you’re running GPT-4o models (e.g., via OpenAI API or your own hosted model like GPT-4o-mini or quantized versions):

A. Ada Lovelace (RTX 40 Series) – Consumer GPUs

• RTX 4090 (24 GB)

  • Best consumer-grade GPU for inference and smaller model fine-tuning.

  • Great for 8-bit or quantized versions of GPT models.

  • Supports FP8 emulation via software.

  
  

• RTX 4080/4070 Ti

  • Cheaper but with less VRAM (12–16 GB).

  • Suitable for smaller models (e.g., LLaMA 7B or GPT-3 style models).

B. RTX 6000 Ada (Workstation)

• 48 GB VRAM, ECC memory, professional drivers.

• Excellent for high-end inference and some training tasks.

• Much more affordable than A100/H100.

C. Older Cards (Limited)

• RTX 3090 / 3090 Ti (24 GB): Still decent for inference of 7B–13B models.

• RTX 3080 (10–12 GB): Limited to very small models or quantized ones.

⚠️ VRAM Considerations

For context, here’s a rough VRAM requirement:

• GPT-2 (~1.5B): 2–4 GB

• GPT-3 (~13B): ~24 GB for full precision

• GPT-4o-sized models (175B+): 300–800 GB+ (impossible on a single GPU)

To run quantized versions (like 4-bit or 8-bit) of GPT-4-class models:

• 13B models: ~8–16 GB VRAM

• 30B models: ~20–32 GB VRAM

• 65B models: ~40–60 GB VRAM