What GPUs should I use for training large language models?

For training large language models, the best GPUs depend mostly on model size, sequence length, and budget. The short version:

Top choices

Best overall for serious LLM training

• NVIDIA H100 80GB
• NVIDIA H200 141GB (even better memory capacity/bandwidth)

These are the current go-to options for training large models efficiently, especially if you’re doing multi-GPU or multi-node training.

Best “pro” option if you need more accessible hardware

• NVIDIA A100 80GB

Still very widely used and well-supported. It’s slower than H100/H200, but extremely common in clusters and cloud environments.

Best budget-ish option for smaller training runs / fine-tuning

• NVIDIA RTX 4090 (24GB)
• NVIDIA RTX 6000 Ada (48GB)

Good for experimentation, fine-tuning, and smaller models, but not ideal for training very large models from scratch.

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What matters most

1. VRAM

More VRAM lets you:

• fit larger models
• use longer context lengths
• increase batch size
• reduce gradient checkpointing/offloading

Typical rough guidance:

• 24GB: small models, LoRA, QLoRA, smaller-scale training
• 48GB: better for medium models and some serious fine-tuning
• 80GB+: preferred for full training of large models

2. Memory bandwidth

LLM training is often bandwidth-bound. Faster memory can matter as much as raw FLOPS.

3. Multi-GPU interconnect

If training across multiple GPUs, look for:

• NVLink / NVSwitch
• fast networking like InfiniBand

This becomes important once a model doesn’t fit on one GPU.

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Practical recommendations by use case

If you want to train a model from scratch

• Minimum serious setup: multiple A100 80GB
• Best setup: multiple H100 80GB or H200 141GB
• Enterprise scale: H100/H200 with NVLink/NVSwitch and fast network

If you want to fine-tune open models

• Best value: RTX 4090
• Better headroom: RTX 6000 Ada
• Easier larger fine-tunes: A100 80GB

If you’re just starting out

• Use what you can access cheaply via cloud or a workstation:
  • 4090
  • A5000 / A6000
  • rented A100/H100 instances when needed

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Avoid these for large LLM training

• Small consumer GPUs with limited VRAM unless you’re only doing tiny models or parameter-efficient fine-tuning
• Older cards with weak memory bandwidth and low VRAM if you plan to scale

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Rule of thumb

If your goal is:

• research / real training: A100 80GB or H100 80GB
• best performance: H100/H200
• best cost for experimentation: RTX 4090
• balanced professional workstation: RTX 6000 Ada

If you want, I can also recommend GPUs based on:

1. your budget,
2. the model size you want to train, or
3. whether you’re doing training vs fine-tuning.