RTX 6000 Pro vs H100 & A100: Best Single-GPU Choice for Fast, Low-Cost LLM Fine-Tuning
Faster, cheaper single-GPU training
NVIDIA is now regularly rolling out new GPUs based on the Blackwell architecture. In a previous article, we saw that the RTX 5090 was the fastest GPU for single-GPU workloads (fine-tuning and inference) with 32 GB of memory or less. We also walked through how to configure PyTorch and major frameworks to run and fine-tune LLMs.
However, 32 GB is often not enough for training LLMs, especially if you want to avoid relying on parameter-efficient tuning methods like LoRA or QLoRA. That’s where the RTX 6000 Pro appears like a good alternative to the RTX 5090. Built on the same core architecture as the RTX 5090 but with 96 GB of VRAM, it’s rapidly gaining adoption, particularly in cloud environments.
Take RunPod (referral link), for example: the RTX 6000 Pro currently costs $1.79/hour, i.e., just a few cents more than an A100 and nearly $1 less than an H100.
A GPU that’s as fast as the RTX 5090, offers triple the memory, and is cheaper than the H100? Sounds too good to be true.
In this article, we’ll benchmark the A100, H100, and RTX 6000 Pro to see how they compare in LLM fine-tuning. Here’s what we’ll cover:
Architecture Comparison: We’ll start by reviewing the core specs of each GPU to understand where the RTX 6000 Pro stands out.
Environment Setup: You’ll learn how to set up PyTorch, FlashAttention, Transformers, Bitsandbytes, and all the standard tooling for fine-tuning on the RTX 6000 Pro.
Spoiler: The same setup I recommended for the RTX 5090 works out-of-the-box here too.Performance Tests: We'll run benchmarks for QLoRA, LoRA, and full fine-tuning, comparing performance across the three GPUs.
In every category, the RTX 6000 Pro emerges as the top choice, making it the most cost-effective option, and the fastest (!), for single-GPU fine-tuning of LLMs.
The following notebook shows how to set up the environment for the RTX 6000 Pro and fine-tune LLMs (full, LoRA, and QLoRA), using Qwen3 for the examples.
Since vLLM installation with the RTX 6000 Pro can be tricky, I’ve included a bonus section that walks you through the complete setup.