Top 10 Cheapest GPUs for AI Training
Top 10 Cheapest GPUs for AI Training in 2025
AI training is hungry work. Whether you're fine-tuning a large language model, training a computer vision classifier, or experimenting with reinforcement learning, GPU compute is the single largest cost in your pipeline. In 2025, with models growing ever larger and training datasets expanding into the terabytes, choosing the right GPU at the right price isn't just a technical decision — it's a financial one.
The good news is that the GPU cloud market has never been more competitive. Peer-to-peer marketplaces like Clore.ai have driven prices down to levels that were unthinkable just two years ago. Whether you need a budget-friendly card for small experiments or a data center behemoth for production training runs, there's a cloud GPU that fits your budget.
In this article, we'll rank the 10 cheapest GPUs for AI training in 2025, based on real marketplace prices from Clore.ai. For each GPU, we'll cover the specs, typical cloud rental price, ideal use cases, and value-for-money rating. Let's dive in.
How We Ranked These GPUs
Our ranking considers three factors:
- Price per hour — the average rental cost on Clore.ai as of February 2025
- Performance per dollar — how much training throughput (measured in tokens/second or images/second) you get relative to cost
- Practical usability — VRAM capacity, software support, and suitability for popular AI frameworks
We focus on single-GPU prices. Multi-GPU configurations scale linearly (or better) and are available on Clore.ai for all models listed.
1. NVIDIA RTX 3090 — The Unbeatable Budget Champion
| Spec | Value |
|---|---|
| VRAM | 24 GB GDDR6X |
| CUDA Cores | 10,496 |
| Tensor Cores | 328 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.06–$0.12/hr |
| Spot Price (GigaSPOT) | $0.03–$0.06/hr |
The RTX 3090 remains the absolute king of budget AI training in 2025. Despite being released in 2020, its 24 GB of VRAM — the same as the much more expensive RTX 4090 — makes it viable for a surprising range of workloads. You can fine-tune 7B parameter LLMs with QLoRA, run Stable Diffusion at full resolution, and train medium-sized vision models without running out of memory.
Best for: Students, hobbyists, Stable Diffusion, small-to-medium model training, inference testing.
Value rating: ★★★★★ — Nothing beats the 3090's price-to-VRAM ratio.
2. NVIDIA RTX 4090 — The Sweet Spot for Serious Work
| Spec | Value |
|---|---|
| VRAM | 24 GB GDDR6X |
| CUDA Cores | 16,384 |
| Tensor Cores | 512 (4th gen) |
| Architecture | Ada Lovelace |
| Clore.ai Avg Price | $0.10–$0.25/hr |
| Spot Price (GigaSPOT) | $0.06–$0.12/hr |
The RTX 4090 offers roughly 1.5–2x the training throughput of the 3090 at a modest price premium. The 4th-generation Tensor Cores support FP8 precision, which accelerates transformer training significantly. With the same 24 GB of VRAM, you get more compute per dollar than almost any other option.
Best for: LLM fine-tuning (7B–13B with quantization), Stable Diffusion XL, computer vision, production inference.
Value rating: ★★★★★ — The best all-rounder for most AI practitioners.
3. NVIDIA RTX 3080 (10/12 GB) — Minimum Viable GPU
| Spec | Value |
|---|---|
| VRAM | 10 or 12 GB GDDR6X |
| CUDA Cores | 8,704 (10GB) / 8,960 (12GB) |
| Tensor Cores | 272 / 280 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.04–$0.08/hr |
| Spot Price (GigaSPOT) | $0.02–$0.05/hr |
The RTX 3080 is the cheapest usable option for AI training. Its 10–12 GB of VRAM is limiting for large models, but perfectly adequate for smaller experiments, image classification, object detection, and inference workloads. At spot prices as low as $0.02/hour, it's essentially free compute.
Best for: Prototyping, small model training, inference, educational projects, batch processing.
Value rating: ★★★★☆ — Incredible price, but VRAM limitations cap its usefulness.
4. NVIDIA RTX 5090 — The New Flagship
| Spec | Value |
|---|---|
| VRAM | 32 GB GDDR7 |
| CUDA Cores | ~21,760 |
| Tensor Cores | 5th gen (FP4 support) |
| Architecture | Blackwell |
| Clore.ai Avg Price | $0.30–$0.50/hr |
| Spot Price (GigaSPOT) | $0.18–$0.30/hr |
The RTX 5090 is the newest consumer GPU from NVIDIA, and it's a beast. With 32 GB of GDDR7 VRAM — 33% more than the 4090 — and support for FP4 inference, it opens the door to training and running larger models than any previous consumer card. Clore.ai hosts 516 RTX 5090 units, more than any other marketplace.
Best for: Large model fine-tuning (13B+ parameters), FP4/FP8 inference, Stable Diffusion 3, next-gen workloads that need >24 GB VRAM.
Value rating: ★★★★☆ — Premium pricing, but the extra VRAM and performance justify it for demanding tasks.
For a deeper look at the RTX 5090's AI capabilities, see our article RTX 5090 for AI: Performance, Availability, and Cloud Rental Prices.
5. NVIDIA A100 40GB — Data Center Workhorse (Budget Tier)
| Spec | Value |
|---|---|
| VRAM | 40 GB HBM2e |
| CUDA Cores | 6,912 |
| Tensor Cores | 432 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.40–$0.70/hr |
| Spot Price (GigaSPOT) | $0.25–$0.40/hr |
The A100 40GB is the entry point to data center-grade AI training. HBM2e memory provides significantly higher bandwidth than consumer GDDR, which matters enormously for training throughput. The 40GB variant is cheaper than the 80GB version and is sufficient for most models up to 13B parameters with mixed precision.
Best for: Production training, multi-GPU distributed training, models requiring high memory bandwidth.
Value rating: ★★★★☆ — More expensive than consumer cards, but memory bandwidth makes it faster for many training workloads.
6. NVIDIA A100 80GB — The Gold Standard
| Spec | Value |
|---|---|
| VRAM | 80 GB HBM2e |
| CUDA Cores | 6,912 |
| Tensor Cores | 432 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.70–$1.00/hr |
| Spot Price (GigaSPOT) | $0.40–$0.65/hr |
With 80 GB of HBM2e, the A100 80GB can handle the largest models that any single GPU can reasonably train. Full fine-tuning of 13B models, LoRA fine-tuning of 70B models, and large batch training are all within reach. On hyperscalers like AWS, this GPU costs $3.00+/hour. On Clore.ai, you can get it for under $1.00.
Best for: LLM fine-tuning (up to 70B with LoRA), large vision models, any workload where 40GB isn't enough.
Value rating: ★★★★☆ — The savings vs. hyperscalers are enormous.
7. NVIDIA RTX A6000 — Professional Middle Ground
| Spec | Value |
|---|---|
| VRAM | 48 GB GDDR6 |
| CUDA Cores | 10,752 |
| Tensor Cores | 336 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.20–$0.40/hr |
| Spot Price (GigaSPOT) | $0.12–$0.25/hr |
The RTX A6000 is an underrated option for AI training. With 48 GB of VRAM — double the RTX 4090 — it can handle models that consumer cards simply can't fit in memory. It lacks the HBM bandwidth of the A100, but at nearly half the price, it's an excellent value for VRAM-hungry workloads.
Best for: Models that need >24 GB but <80 GB VRAM, 3D rendering, professional visualization combined with AI.
Value rating: ★★★★☆ — The best VRAM-per-dollar outside of data center cards.
8. NVIDIA H100 — Maximum Performance
| Spec | Value |
|---|---|
| VRAM | 80 GB HBM3 |
| CUDA Cores | 14,592 |
| Tensor Cores | 456 (4th gen) |
| Architecture | Hopper |
| Clore.ai Avg Price | $1.00–$2.00/hr |
| Spot Price (GigaSPOT) | $0.70–$1.20/hr |
The H100 is the fastest GPU money can rent for AI training. Its 4th-generation Tensor Cores, HBM3 memory with 3.35 TB/s bandwidth, and transformer engine make it the undisputed champion for training large language models. On AWS, an H100 instance costs $4–$5/hour. On Clore.ai, you can often get one for $1.00–$1.50.
Best for: Large-scale LLM training, research requiring maximum throughput, time-sensitive training runs.
Value rating: ★★★☆☆ — The most expensive option on this list, but the fastest. Choose when time matters more than money.
9. NVIDIA RTX 3070 (8 GB) — Ultra-Budget Option
| Spec | Value |
|---|---|
| VRAM | 8 GB GDDR6 |
| CUDA Cores | 5,888 |
| Tensor Cores | 184 (3rd gen) |
| Architecture | Ampere |
| Clore.ai Avg Price | $0.03–$0.06/hr |
| Spot Price (GigaSPOT) | $0.01–$0.03/hr |
At $0.01–$0.03/hour on spot, the RTX 3070 is practically free. The 8 GB VRAM severely limits training capabilities, but it's perfectly usable for inference, small experiments, and learning purposes. If you're a student running your first PyTorch tutorials, this is all you need.
Best for: Learning, inference, small experiments, batch processing where VRAM isn't a constraint.
Value rating: ★★★☆☆ — Incredible price, significant VRAM limitations.
10. NVIDIA L40S — The Dark Horse
| Spec | Value |
|---|---|
| VRAM | 48 GB GDDR6 |
| CUDA Cores | 18,176 |
| Tensor Cores | 568 (4th gen) |
| Architecture | Ada Lovelace |
| Clore.ai Avg Price | $0.50–$0.80/hr |
| Spot Price (GigaSPOT) | $0.30–$0.50/hr |
The L40S is NVIDIA's Ada Lovelace-based data center GPU designed for AI inference and training. With 48 GB of VRAM and 4th-gen Tensor Cores, it sits between the RTX 4090 and A100 in both performance and price. It's increasingly available on Clore.ai and offers an interesting middle ground.
Best for: Inference serving, medium-to-large model training, workloads that need >24 GB but don't justify A100 pricing.
Value rating: ★★★★☆ — Good VRAM, good compute, reasonable price.
Quick Comparison Table
| Rank | GPU | VRAM | Clore.ai Avg $/hr | Spot $/hr | Best For |
|---|---|---|---|---|---|
| 1 | RTX 3090 | 24 GB | $0.06–$0.12 | $0.03–$0.06 | Budget training, SD |
| 2 | RTX 4090 | 24 GB | $0.10–$0.25 | $0.06–$0.12 | All-rounder |
| 3 | RTX 3080 | 10/12 GB | $0.04–$0.08 | $0.02–$0.05 | Prototyping, inference |
| 4 | RTX 5090 | 32 GB | $0.30–$0.50 | $0.18–$0.30 | Next-gen workloads |
| 5 | A100 40GB | 40 GB | $0.40–$0.70 | $0.25–$0.40 | Production training |
| 6 | A100 80GB | 80 GB | $0.70–$1.00 | $0.40–$0.65 | Large model training |
| 7 | RTX A6000 | 48 GB | $0.20–$0.40 | $0.12–$0.25 | VRAM-hungry tasks |
| 8 | H100 | 80 GB | $1.00–$2.00 | $0.70–$1.20 | Maximum throughput |
| 9 | RTX 3070 | 8 GB | $0.03–$0.06 | $0.01–$0.03 | Learning, inference |
| 10 | L40S | 48 GB | $0.50–$0.80 | $0.30–$0.50 | Inference, mid-range |
How to Choose the Right GPU for Your Budget
Under $0.10/hour: RTX 3090 or RTX 3080
If your budget is tight, the RTX 3090 is the undisputed champion. At $0.06–$0.12/hour on Clore.ai (and as low as $0.03 on spot), it delivers 24 GB of VRAM and respectable training performance. The RTX 3080 is even cheaper but limited by its VRAM.
$0.10–$0.30/hour: RTX 4090 or RTX A6000
This is the sweet spot for most practitioners. The RTX 4090 gives you the best balance of training speed and cost. If you need more VRAM (48 GB), the A6000 is a compelling option in a similar price range.
$0.30–$1.00/hour: RTX 5090, A100, or L40S
For serious training runs, the A100 80GB offers the best combination of VRAM and memory bandwidth. The RTX 5090 is ideal if you want next-gen consumer performance with 32 GB of VRAM. The L40S splits the difference.
$1.00+/hour: H100
When time is money and you need maximum throughput, the H100 is the only choice. At Clore.ai's prices, it's 2–3x cheaper than the same GPU on hyperscalers.
Saving Even More with Spot Pricing
Every GPU on this list is available at reduced spot prices through Clore.ai's GigaSPOT auction system. Spot instances can save you 50–70% compared to on-demand pricing, making already cheap GPUs absurdly affordable. The trade-off is potential interruption, but for training jobs that support checkpointing (which most modern frameworks do), this is a no-brainer.
Why Clore.ai Has the Cheapest GPU Prices
The prices listed in this article are available because of Clore.ai's unique market structure:
- Ultra-low platform fees (as low as 1.8% with PoH) — hosts keep almost all their earnings, incentivizing competitive pricing
- P2P marketplace — thousands of hosts competing on price drives costs down naturally
- Global supply — hosts from around the world contribute GPUs, increasing supply and reducing prices
- GigaSPOT auctions — spot pricing captures idle capacity at steep discounts
On traditional cloud providers, the same GPUs cost 3–10x more. For a detailed comparison, see our GPU Cloud Pricing Comparison 2025.
Conclusion
AI training doesn't have to break the bank. In 2025, the combination of competitive P2P marketplaces and a growing supply of GPUs has made cloud compute more affordable than ever. Whether you're spending $0.03/hour on a spot RTX 3090 or $1.50/hour on an H100, Clore.ai offers the lowest prices in the market thanks to its 1.6% platform fee and vibrant P2P marketplace.
Our top pick for most users: The RTX 4090 at $0.10–$0.25/hour. It offers the best balance of price, performance, and VRAM for the widest range of AI workloads.
For budget-constrained beginners: The RTX 3090 at $0.06–$0.12/hour. 24 GB of VRAM at the lowest possible price.
For cutting-edge work: The RTX 5090 at $0.30–$0.50/hour. 32 GB of GDDR7 and the latest architecture.
Ready to start training? Browse available GPUs on Clore.ai and launch your first training run in minutes.