NVIDIA A100 vs. RTX 4090: Best GPU for LLM Fine-Tuning (2026)
Quick answer: If your model fits in 24 GB and you already own a desktop, a 4090 is great. The moment you need more memory, multi-GPU scale-out, or you'd rather avoid a $2K+ hardware bill, rent an A100 80 GB at $0.78/hr on Thunder Compute and get to work immediately.
Spec sheet at a glance
What is LLM fine-tuning?
LLM fine-tuning is the process of adapting a pre-trained model to your data so it follows your domain, tone, or task. It costs money in GPU hours and storage, and it costs time in experiment cycles. However in return you get higher accuracy and better alignment with your product without training a model from scratch.
VRAM requirements for fine-tuning
Fine-tuning GPT-style models is mostly a memory problem. A single 30B-parameter model needs about 60 to 65 GB just to load with 8-bit weights; mixed precision or LoRA adapters push that higher. The A100 80 GB handles this on one card, or you can shard across multiple A100s via NVLink. With only 24 GB, a 4090 forces heavy checkpointing, CPU offload, or model downsizing, which slows iteration and complicates your codebase.
Raw speed vs. usable speed
Benchmarks that include I/O and optimizer states show full fine-tunes running 3 to 4 times faster on an A100 than a 4090 once the model actually fits. When the 4090 is faster, for example CNNs that fit comfortably in 24 GB, the gap is often under 20 percent. For LLMs, memory bottlenecks dominate.
Buying an RTX 4090 vs. Renting an A100
<ul> <li><strong>Buying a 4090:</strong> Initial cost of over $3,000 up front; resale uncertain.</li> <li><strong>Buying an A100:</strong> $7,000 to $12,000 per card, plus a dual-socket server and datacenter-grade power and cooling.</li> <li><strong>Renting an A100 on Thunder:</strong> 80 GB = $0.78/hr. At around 350 GPU-hours per month you still spend under the retail price of one 4090, and you can burst to eight A100s when needed, then spin them down.</li> </ul>
Use our transparent pricing page (/pricing) to see the exact hourly cost in your region and estimate your break-even point.
When to Use RTX 4090 vs. NVIDIA A100 for AI Projects
Try it yourself
Ready to see how much larger a model you can fine-tune with an A100? Spin up a GPU in 60 seconds in VSCode at www.thundercompute.com. No commitments, just cheap, on-demand horsepower for your next experiment.
FAQs
Q: Is the 4090 "overkill" for most AI tasks?A: Not if your model fits in 24 GB. But if you need more VRAM sometimes, renting an A100 when you need it is cheaper than owning both.
Q: How many A100s can I chain together on Thunder?A: Up to eight in a single node with NVLink, or scale horizontally with our high-bandwidth fabric.
Q: Can I start small and scale?A: Yes! Begin with one A100 40 GB, snapshot your disk, then relaunch on a larger multi-GPU node when your project grows.
Q: Can I fine-tune LLMs on a 24 GB 4090?A: Yes for many 7B to 13B models with LoRA or QLoRA. Larger models often require more VRAM or careful offloading that slows training.
Q: Do I need NVLink for multi-GPU fine-tuning?A: It depends on the model size and parallelism method. NVLink helps with model parallel training, while data parallel setups can work without it.
Q: How much storage should I budget for fine-tuning?A: Plan for your dataset size plus checkpoints and logs. Many projects need 100 GB or more once multiple runs and checkpoints are included.
Still choosing? Test-drive an A100 today and keep your experiments flowing, without melting your credit card.