Rent MI250X 128GB
Compare live on-demand and spot rental prices across 102+ cloud providers. Dual-die AMD CDNA 2 GPU. 128GB HBM2e across two dies. Predecessor to MI300X. Available on AWS (p4de) and select cloud providers at competitive pricing.
| Provider | Region | On-Demand /hr | Spot /hr | Monthly est. | Avail. |
|---|---|---|---|---|---|
Choosing the right billing model for MI250X 128GB
Provision and terminate at any time. Ideal for development, short experiments, and workloads with unpredictable duration.
Instances can be reclaimed when demand spikes. Best for fault-tolerant batch jobs, training with checkpointing, and preprocessing.
Lock in a rate for 1–3 months. Right for sustained production inference or long training runs where cost predictability matters.
Best use cases
- Large model training
- Memory-bound HPC
- AMD ROCm workloads
MI250X 128GB — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
MI250X 128GB Rental Guide
The MI250X is best suited for workloads that need large VRAM capacity at competitive cost, particularly on AWS p4de instances where it is available as a managed service. Its 128GB HBM2e makes it one of the few single-card options for 70B+ parameter models at FP16 without quantization. For LLM inference serving, the 3,277 GB/s bandwidth delivers strong tokens-per-second throughput for memory-bound autoregressive generation — comparable to H100 SXM5 bandwidth at a lower rental rate on AWS. The absence of FP8 support means inference efficiency is lower than H100 for quantized workloads, but for BF16 inference the MI250X is competitive.
The dual-die NUMA topology requires attention during setup. When running PyTorch on ROCm, set ROCR_VISIBLE_DEVICES and verify that your model fits within a single die's 64GB if you want to avoid cross-die traffic. For models between 64GB and 128GB, cross-die access is unavoidable — benchmark your specific model and batch size to confirm acceptable throughput. AWS p4de instances expose 8 MI250X cards per node with Elastic Fabric Adapter (EFA) networking for multi-node training; this is the primary deployment pattern for large-scale training runs on MI250X.
For teams evaluating the MI250X against NVIDIA alternatives: the A100 80GB offers 80GB HBM2e at 2,039 GB/s bandwidth with mature CUDA support and FP8 via software emulation; the H100 80GB offers 80GB HBM3 at 3,350 GB/s with native FP8 and the Transformer Engine. The MI250X's 128GB capacity is its unique advantage — if your model fits in 80GB, the H100 is likely the better choice for CUDA ecosystem compatibility. If you need 128GB+ without multi-card tensor parallelism, the MI250X (or its successor MI300X at 192GB) is the primary option. Compare current rental rates across providers using the pricing table above before committing.
Frequently Asked Questions
How much does it cost to rent a MI250X 128GB?
MI250X 128GB on-demand rental prices vary by provider and region. On-demand rates typically range based on availability and provider margins — use the comparison table above to see current live rates across all providers. Spot instances are generally 40–70% cheaper than on-demand but can be interrupted. Monthly cost estimates (hourly rate × 730 hours) are shown in the table for sustained workloads.
Which cloud provider has the cheapest MI250X 128GB?
The cheapest MI250X 128GB provider changes as providers update their pricing. The comparison table above shows live rates sorted by price, so the cheapest option is always at the top. Factors beyond headline price include region (latency to your users), availability (high/medium/low), and billing granularity (per-second vs per-hour minimums).
What can I run on a MI250X 128GB?
With 128GB of HBM2e, the MI250X 128GB can run LLM models up to approximately 64B parameters at FP16, 128B at INT8, or 256B at INT4/GGUF quantization. Common workloads include: Large model training, Memory-bound HPC, AMD ROCm workloads. Dual-die AMD CDNA 2 GPU. 128GB HBM2e across two dies. Predecessor to MI300X. Available on AWS (p4de) and select cloud providers at competitive pricing.
Should I use on-demand or spot pricing for MI250X 128GB?
Spot instances save 40–70% vs on-demand but can be interrupted when the provider needs capacity back. Use spot for: batch inference jobs, training runs with checkpointing, preprocessing pipelines, and any workload that can tolerate interruption and restart. Use on-demand for: production inference serving, interactive workloads, and jobs that cannot be interrupted. Most providers bill per second, so short on-demand jobs are not penalized by hourly minimums.
How does the MI250X 128GB compare to the H100 for cloud rental?
The H100 80GB delivers 1,979 TFLOPS FP16 with 3,350 GB/s HBM3 bandwidth, compared to the MI250X 128GB's 383 TFLOPS FP16 and 3277 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the MI250X 128GB. For workloads that fit within 128GB and don't require FP8 precision, the MI250X 128GB often delivers better cost-per-token than the H100.
What is the memory bandwidth of the MI250X 128GB and why does it matter?
The MI250X 128GB has 3277 GB/s of memory bandwidth. For LLM inference, memory bandwidth is often more important than raw TFLOPS — each autoregressive token generation reads the full model weight matrix from VRAM, so bandwidth directly determines tokens-per-second throughput. Higher bandwidth means faster inference for the same model at the same batch size. For batch inference (processing many requests simultaneously), compute throughput becomes more important.
Can I use the MI250X 128GB for Stable Diffusion or image generation?
Yes — the MI250X 128GB is capable for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the MI250X 128GB's 47.9 TFLOPS FP32 throughput determines images-per-second. The 128GB VRAM fits SDXL (requires ~6GB) and most ControlNet pipelines. For high-throughput image generation at scale, compare cost-per-image across providers using the GPU cost calculator.