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MI300X on Nebius: Pricing, Specs & How to Rent

Deploybase · September 24, 2025 · GPU Pricing

Contents

MI300X GPU Overview

The MI300X on Nebius pricing reflects growing demand for AMD alternatives to NVIDIA's dominant GPU lineup. The MI300X is AMD's latest data center accelerator featuring 192 GB of HBM3 memory, 5.3 TB/s memory bandwidth, and 2.61 petaflops (2,610 TFLOPS) peak FP8 tensor throughput. As of March 2026, the MI300X targets teams seeking cost-effective alternatives for LLM training and inference without NVIDIA ecosystem lock-in.

MI300X specifications:

  • Memory: 192 GB HBM3
  • Memory bandwidth: 5.3 TB/s
  • Tensor throughput (FP8): 2.61 PFLOPS (2,610 TFLOPS)
  • Tensor cores: 19,456 matrix engines
  • GPU-GPU interconnect: Infinity Fabric with 576 GB/s bandwidth
  • TDP: 750W
  • Manufacturing process: 5nm

Nebius MI300X Pricing & Availability

Nebius is the primary European distributor of AMD MI300X compute. Pricing starts at approximately $2.50 per GPU hour for single-instance rentals, with volume discounts for larger clusters. This undercuts NVIDIA's H100 pricing (typically $2.86-3.78 per hour) while offering superior memory capacity and bandwidth.

Nebius MI300X pricing structure:

  • Single MI300X: $2.50/hour
  • 4-GPU clusters: $2.30/hour per GPU (bulk discount)
  • 8-GPU clusters: $2.10/hour per GPU
  • Commitment discount (monthly): 20% off
  • Commitment discount (annual): 35% off
  • Data transfer (egress): Free within EU, $0.10/GB to external regions

Nebius targets European companies prioritizing GDPR compliance and data sovereignty. The platform commits to storing all training data within European datacenters (primarily Germany and Netherlands).

Performance vs NVIDIA

The MI300X surpasses H100 on memory-bound workloads and inference tasks. 192 GB of HBM3 supports larger batch sizes and longer context windows for language models. Tests show MI300X performing 10-15% faster than H100 on typical LLM inference, primarily due to memory bandwidth advantages.

Performance comparison:

  • H100 SXM memory: 80 GB HBM3
  • MI300X memory: 192 GB HBM3
  • H100 SXM memory bandwidth: 3.35 TB/s
  • MI300X memory bandwidth: 5.3 TB/s
  • 70B LLM inference (H100): 8-10 tokens/sec
  • 70B LLM inference (MI300X): 9-12 tokens/sec
  • 200B+ LLM training (MI300X): 15% higher throughput

Trade-offs to consider:

  • NVIDIA ecosystem is larger (more optimized frameworks)
  • AMD ROCm software stack is improving but less mature
  • MI300X offers better memory scaling for large models
  • NVIDIA has superior multi-GPU interconnect speed (NVLink 5 vs Infinity Fabric)

Renting MI300X on Nebius

Nebius operates a portal similar to AWS. Rental process:

  1. Create Nebius account (EU residence required or proof of business EU operation)
  2. Verify identity and payment method
  3. Go to GPU Instances
  4. Select MI300X from available options
  5. Choose cluster size (single GPU to 8-GPU cluster)
  6. Select region (Frankfurt or Amsterdam datacenters)
  7. Configure storage (NVMe, HDD, or persistent volume)
  8. Set root password or SSH key
  9. Choose billing cycle (hourly, monthly, annual)
  10. Review cost and deploy

Provisioning takes 3-5 minutes. SSH access becomes available after deployment completes. Nebius provides a Jupyter proxy at https://[instance-ip]:8888 for interactive development.

MI300X instances come pre-configured with:

  • ROCm 6.1 runtime
  • PyTorch with ROCm backend pre-compiled
  • TensorFlow 2.15+ with AMD acceleration
  • Custom Nebius libraries for multi-GPU orchestration

FAQ

How does ROCm stability compare to CUDA? ROCm has matured significantly. For standard PyTorch workloads, ROCm 6.1+ is production-ready. Custom kernel development remains more complex than CUDA, requiring familiarity with HIP (AMD's CUDA equivalent).

Can I train NVIDIA-native models on MI300X? Most PyTorch models trained on NVIDIA GPUs work on MI300X without code changes (via PyTorch's abstraction). Performance may vary by 5-20% depending on custom CUDA kernels in the model. Models using flash-attention typically require recompilation.

Is Nebius suitable for U.S. companies? Nebius is EU-based and primarily serves European customers. U.S. companies can rent capacity, but data must be hosted in EU datacenters (GDPR implications). This is not ideal for regulated U.S. workloads requiring domestic infrastructure.

What's the multi-GPU scaling efficiency? MI300X in 8-GPU configurations achieves 88-92% scaling efficiency on typical distributed training (90% ideal would mean 8x speedup). This is comparable to NVIDIA setups running NVLink, making MI300X competitive for large distributed training.

Are there pre-built container images available? Nebius provides Docker images with pre-installed ROCm and popular frameworks. Docker Hub contains community-maintained AMD MI300X images. Internal benchmarking recommends using Nebius-provided images for optimal performance.

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