Apache MXNet was a fast, scalable deep learning framework once backed by AWS. Important: MXNet was retired to the Apache Attic in 2023 and no longer receives updates. If you still run MXNet models, you can host them on a GPU server, but new projects should use PyTorch, JAX or TensorFlow.
Table of Contents
Which GPU Do You Need for Apache MXNet?
| Workload | Recommended GPU | Why |
|---|---|---|
| Legacy MXNet inference | RTX A4000 / A5000, V100 | CUDA 11-era cards match old builds |
| Legacy MXNet training | A100 40/80 GB | Well supported by CUDA 11 |
| Migrated PyTorch/ONNX models | RTX 4090, A100, H100 | Modern stacks, best speed |
Best Apache MXNet 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 Apache MXNet 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.
Should You Still Use MXNet?
Only for legacy models. Because MXNet is retired, it does not support recent CUDA versions or new GPUs as well as active frameworks. The safest approach is to pin an older CUDA/Docker image for existing models and plan a migration.
Migration paths: export models to ONNX and serve them with ONNX Runtime or NVIDIA Triton, or port the code to PyTorch or Keras 3.
How to Set Up Apache MXNet on a GPU Server
# Run legacy MXNet in a pinned container on any NVIDIA GPU server
docker run --gpus all -it --rm python:3.8 bash
pip install mxnet-cu112 # older CUDA build; needs a compatible driver
# Recommended: export to ONNX and serve with ONNX Runtime GPU
pip install onnxruntime-gpuFAQ
No. Apache MXNet was moved to the Apache Attic in 2023 and is no longer maintained.
PyTorch is the most common replacement; Keras 3 or JAX are also good choices. Export existing models to ONNX to keep serving them.
Older CUDA 11-era GPUs (V100, A100, RTX 30-series, RTX A-series) are the safest match for legacy MXNet builds.
Conclusion
MXNet is retired, so host legacy models in pinned containers on a GPU server from RunPod, Lambda or GPU Mart, and plan a move to PyTorch or ONNX Runtime.
Prices were checked in September 2026 and change often. Always confirm current pricing on the provider’s website before you order.