Instinct MI355X
AMD288 GBHBM3E per GPU
- Per node
- 8 GPUs, 2.3 TB of GPU memory
- Software
- PyTorch on ROCm
- MLPerf
- Llama 2 70B fine-tune in 10.18 min on 8 GPUs
- Price
- A$9.35 per GPU-hour on demand including GST, A$4.95 on a 36-month term
AMD MI355X and NVIDIA B300 GPUs, hosted in Australia.
Train on current-generation GPUs with 288 GB of HBM3E each, eight to a node. Neither is offered in AWS, Google Cloud or Azure’s Australian regions. MI355X is A$9.35 per GPU-hour including GST on demand; B300 is quoted on request.
Two GPUs, eight to a node
Both carry 288 GB of memory per GPU, 3.6× an 80 GB H100, so bigger models and longer contexts fit on fewer GPUs. Choose MI355X for PyTorch on ROCm, or B300 for CUDA-only code and FP4 training.
B300
NVIDIA288 GBHBM3E per GPU
- Per node
- 8 GPUs, 2.3 TB of GPU memory
- Software
- The CUDA stack, including FP4 training
- Good for
- CUDA-only code and FP4 training
- Price
- On request
Bring your stack
Your training scripts come with you. On MI355X, a Maincode data scientist helps port anything CUDA-specific in your first run.
- Train
- PyTorchTransformersTRLPEFTAccelerateAxolotlUnslothLLaMA-Factorytorchtitan
- RL and kernels
- verlFlashAttentionLiger Kernelbitsandbytes
- Evaluate and serve
- vLLMSGLang
- Models
- QwenLlamaGemmagpt-ossMistralDeepSeek
One 70B fine-tune, priced in Australia
The GPU-hour price is half the story.
The other half is how long the run takes.
Vendor-submitted MLPerf Training results, times each provider’s on-demand GPU-hour price in its Australian region. Your workload will differ.
2.7× faster
than the best published 8× H100 result: 10.18 against 27.54 minutes
4× cheaper
per run than AWS Sydney on demand: A$11.54 against A$47.06, excluding GST
GPU questions
More detail, examples and troubleshooting are in the researcher documentation.
AMD or NVIDIA: does my code need to change?
Most PyTorch and Hugging Face training code runs on MI355X as it is. Code that depends on CUDA-only kernels can run on B300 instead, or a Maincode data scientist can help port it when you start.
Which models can I train?
Any open-weight model your code can load, including the Qwen, Llama, Gemma, gpt-oss, Mistral and DeepSeek families. Use LoRA or QLoRA for quick adaptation, or full-parameter training when you need it. With 288 GB per GPU, larger models fit on fewer GPUs.
Do I need to know Slurm or run a cluster?
No. Your coding agent handles submission, monitoring and results through the MCP endpoint, and Maincode runs the hardware and scheduler. If you already know HPC, native Slurm is there too. See the job guide.
How is billing worked out?
Per GPU-hour. On-demand use is billed per second at A$9.35 including GST (A$8.50 excluding GST), reserved nodes cost less, and preemptible capacity is A$4.675 including GST (A$4.25 excluding GST) for jobs that checkpoint. CPU-only allocations cost A$0.066 per vCPU-hour including GST (A$0.06 excluding GST), with up to 4 GiB RAM per vCPU included, billed per allocated second; GPU allocations include their CPU and memory. Prices are in Australian dollars. Transfer and support are included, subject to fair-use limits.
How much does NVIDIA B300 cost?
B300 pricing is on request. Email us about the run you have in mind and we’ll quote it.
Bring your training.
Own the model.
Create your account, add compute credits and connect your coding agent. You pay by the GPU-hour, and every checkpoint is yours to download.
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