RTX 6000 Pro vs H100 & A100: Best Single-GPU Choice for Fast, Low-Cost LLM Fine-Tuning
Faster, cheaper single-GPU training
In our last deep dive, the RTX 5090 came out as the fastest GPU for single-GPU workloads under 32 GB of VRAM, perfect for fine-tuning and inference at smaller scales.
But once you push into full LLM training territory, 32 GB feels cramped. Parameter-efficient tuning methods like LoRA or QLoRA help, but they’re not always enough when you want maximum accuracy and minimal compromises.
That’s where the RTX 6000 Pro enters the picture. Same core architecture as the 5090, triple the memory (96 GB), and a surprising rental price: just $1.79/hour on RunPod (referral link), only slightly more than an A100, and far less than an H100.
On paper, it sounds almost too good to be true. But here’s the catch:
Raw specs don’t tell the full story. Some GPUs underperform their numbers.
Environment setup can make or break your speed, especially with vLLM and FlashAttention.
Pricing sweet spots like this often vanish in weeks as demand spikes. Note (August 10th, 2025): Since I wrote this article, the cost of the RTX 6000 Pro increased on almost all platforms that I check, e.g., from $1.79/hour to $1.96/hour on RunPod!
I ran full head-to-head benchmarks, A100 vs H100 vs RTX 6000 Pro, across QLoRA, LoRA, and full fine-tuning, using Qwen3 as our test case.
In this article, you will find how to:
Cut costs by 30–40% without losing speed
Avoid multi-day debugging of PyTorch + vLLM on RTX 6000 Pro
Lock in a rental setup that just works before prices climb
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: