NVIDIA A100 vs. RTX 4090: Which GPU Offers Better Value for Fine-Tuning?
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 $2 K+ hardware bill, rent an A100 (40 GB at $0.66/hr or 80 GB at $0.78/hr on Thunder Compute) and get to work immediately.
Spec sheet at a glance
| A100 40 GB / 80 GB | RTX 4090 24 GB | |
|---|---|---|
| Memory (GB) | 40 / 80 HBM2e | 24 GDDR6X |
| Memory bandwidth | > 2 TB/s | ~ 1 TB/s |
| Tensor FP16/8 (peak) | ~ 312 TFLOPs | ~ 90 TFLOPs |
| Multi‑GPU NVLink | Yes | No |
| Street price (buy) | $7 K – $12 K on eBay | ≈ $2,819 (May 2025) |
| Rent on Thunder Compute | $0.66/hr (40 GB) • $0.78/hr (80 GB) | N/A |
Why VRAM rules fine-tuning
Fine-tuning GPT-style models is mostly a memory problem. A single 30 B-parameter model needs ~ 60–65 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 off-load, or model downsizing; slowing iteration and complicating your codebase.
Raw speed vs. usable speed
Benchmarks that include I/O and optimizer states show full fine-tunes running 3–4× faster on an A100 than a 4090 once the model actually fits. When the 4090 is faster (e.g., CNNs that fit comfortably in 24 GB), the gap is often < 20 %. For LLMs, memory bottlenecks dominate.
Cost math: buy vs. rent
- Buying a 4090:
Cash outlay ≈ $2 – 3 K up-front; resale uncertain. - Buying an A100:
$7 – 12 K per card; and you still need a dual-socket server plus datacenter-grade power/cooling. - Renting an A100 on Thunder:
40 GB = $0.66/hr, 80 GB = $0.78/hr. At ~ 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.
Use our transparent pricing page (/pricing) to see the exact hourly cost in your region and estimate your break-even point.
When to choose each GPU
| Your workload | Best pick | Why |
|---|---|---|
| Fine‑tuning 7 B–13 B models, hobby budget | 4090 | Fits in 24 GB, good FP32 throughput |
| Fine‑tuning Llama 2 34 B+ or Mixtral | A100 80 GB | Fits in memory; NVLink scales |
| Multi‑node training / model parallel | A100 cluster | NVSwitch / NVLink, MIG for smaller jobs |
| Inference only, batch size less than 4 | 4090 or A100 40 GB | Both work; 4090 cheaper if you already own it |
| Bursty, pay‑as‑you‑go research | Rent A100 | Zero cap‑ex, instant scale |
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.
Still choosing? Test-drive an A100 today and keep your experiments flowing—without melting your credit card.