Keras is the high-level deep learning API that makes building neural networks simple. Since Keras 3 it is multi-backend: the same Keras code can run on TensorFlow, JAX or PyTorch, and all three use the GPU for fast training.
Table of Contents
Which GPU Do You Need for Keras?
| Workload | Recommended GPU | Why |
|---|---|---|
| Learning and small models | Free Kaggle/Colab T4, RTX A4000 (16 GB) | Cheap, enough VRAM for tutorials |
| Computer vision, mid-size models | RTX 4090 (24 GB) / RTX A5000 | Fast FP16/BF16, 24 GB |
| Fine-tuning LLMs (7–14B, LoRA) | RTX 4090, RTX 5090, A6000 | 24–48 GB VRAM |
| Training large models | A100 80GB, H100, H200 | HBM memory, NVLink, BF16/FP8 |
| Multi-GPU training | 8× A100/H100 with NVLink | Fast interconnect for data/model parallelism |
Best Keras GPU Hosting Providers
1. RunPod
- Per-second billing in 30+ regions
- Templates: vLLM, ComfyUI, PyTorch, Ollama, Jupyter
- Serverless AI endpoints
- RTX 3090 $0.22/hr · RTX 4090 $0.34/hr · A100 $1.19/hr · H100 $1.99/hr · H200 $3.59/hr (Community Cloud)
Pros
- Very cheap consumer GPUs
- Huge GPU choice up to B200
- Fast start-up
Cons
- Storage billed when stopped
- Community hosts vary
RunPod templates come with CUDA and popular frameworks pre-installed, so you can start Keras work in a minute. RTX A5000 from $0.16/hour, RTX 4090 $0.34/hour, A100 from $1.19/hour, H100 from $1.99/hour.
2. Lambda
- On-demand GPU cloud built for AI
- Lambda Stack pre-installed
- 1× to 8× GPU instances and clusters
- A6000 $1.09/hr · A100 40GB $1.99/hr · GH200 $2.29/hr · H100 SXM from $3.99/hr · B200 from $6.69/hr
Pros
- Reliable data-center GPUs
- No egress fees
- Simple pricing
Cons
- GPUs can sell out
- No monthly servers
Lambda Stack includes NVIDIA drivers, CUDA, cuDNN, PyTorch and TensorFlow on every instance. A6000 $1.09/hour, A100 from $1.99/hour, H100 from $3.99/hour.
3. GPU Mart
- Dedicated GPU servers and GPU VPS (US)
- GPUs from P1000 to RTX 5090, RTX PRO 6000, A100 and H100
- One-click AI apps: Ollama, Stable Diffusion, ComfyUI
- GPU VPS from $21/mo · RTX 4090 $409/mo · RTX 5090 from $419/mo · A100 80GB $1,559/mo · H100 $2,099/mo
Pros
- Lowest monthly prices for dedicated GPUs
- Full root/admin access, Windows or Linux
- Multi-GPU servers available
Cons
- Monthly billing only
- US data centers only
Monthly dedicated servers with full root access: RTX A4000 VPS $119, RTX 4090 $409, A100 80GB $1,559, 4× A100 $1,899.
4. HOSTKEY
- GPU servers in the EU, UK and US
- RTX 4090, RTX 5090, RTX PRO 6000, A100, H100
- Pre-installed AI stack: Ollama, Open WebUI, ComfyUI
- GTX 1080 Ti from €70/mo · RTX 4090 €279/mo · RTX 5090 €590/mo · A100 80GB €1,300/mo · H100 €1,590/mo
Pros
- Hourly or monthly billing
- GDPR-friendly EU hosting
- Big-VRAM options
Cons
- Popular GPUs sell out
- Setup slower than cloud pods
EU GPU servers with PyTorch/TensorFlow images; RTX 4090 from €279/month, A100 80GB €1,300, H100 €1,590.
5. Vast.ai
- GPU marketplace, 68+ GPU types
- On-demand, interruptible and reserved pricing
- Docker templates for AI tools
- Market pricing: RTX 4090 from ~$0.30/hr · A100 80GB from ~$0.43/hr · interruptible 50%+ cheaper
Pros
- Often the lowest prices
- Per-second billing
- Great for experiments
Cons
- Host quality varies
- Not for strict compliance
Marketplace GPUs with per-second billing; use interruptible instances with checkpoints for cheap training runs.
Keras 3 and GPUs
With Keras 3 you set the backend with an environment variable (KERAS_BACKEND=jax, tensorflow or torch). JAX is often the fastest on NVIDIA GPUs, while PyTorch gives access to its huge ecosystem.
KerasHub (formerly KerasNLP/KerasCV) provides pretrained models such as Gemma and Stable Diffusion, which run well on 16–24 GB GPUs.
How to Set Up Keras on a GPU Server
pip install keras "jax[cuda12]"
export KERAS_BACKEND=jax
python -c "import keras, jax; print(keras.__version__, jax.devices())"FAQ
Not anymore. Keras 3 runs on TensorFlow, JAX or PyTorch.
An RTX 4090 or RTX A5000 (24 GB) covers most Keras projects; use A100/H100 for large models.
Conclusion
Keras 3 runs anywhere PyTorch, JAX or TensorFlow runs. Use RunPod or Lambda for quick GPU access, or GPU Mart for an always-on server.
Prices were checked in September 2026 and change often. Always confirm current pricing on the provider’s website before you order.