ultraData Center

MI250 GPU

The AMD Instinct MI250 is a data center GPU accelerator with 128GB HBM2e memory for AI and HPC workloads.

VRAM 128GB
TDP 500W
Contact providers for pricing
MI250 GPU

Cloud Pricing

No pricing data available for this GPU at the moment.

Prices updated daily. Last check: 4/8/2026

Performance

FP16
362 TFLOPS
Bandwidth
3276 GB/s

Strengths & Limitations

  • 128 GB HBM2e memory capacity enables training of large models without memory constraints
  • 3.2 TB/s memory bandwidth supports memory-intensive computational workloads
  • 362.1 TFLOPs FP16 performance delivers high throughput for AI training tasks
  • 45.3 TFLOPs FP64 performance per GPU die supports scientific computing applications
  • 8 Infinity Fabric Links with 100 GB/s bandwidth enable efficient multi-GPU scaling
  • AMD ROCm ecosystem provides open-source software stack without vendor lock-in
  • TSMC 6nm FinFET manufacturing process offers improved power efficiency over older nodes
  • 500W TDP requires substantial cooling infrastructure and power delivery
  • AMD ROCm ecosystem has smaller software library compared to CUDA
  • Released in 2021, now superseded by newer GPU generations from both AMD and NVIDIA
  • OAM form factor limits deployment flexibility compared to PCIe add-in cards
  • May be overkill for inference workloads that don't require 128 GB memory capacity

Key Features

AMD CDNA 2 Architecture
HBM2e memory subsystem
AMD Infinity Fabric interconnect
AMD ROCm software ecosystem
Mixed-precision compute support (FP16/FP32/FP64/bfloat16)
Integer precision support (INT4/INT8)
PCIe 4.0 connectivity
OAM module design

About MI250

The AMD MI250 is a high-performance data center accelerator built on the CDNA 2 architecture and manufactured using TSMC's 6nm FinFET process. Released in November 2021, the MI250 serves as AMD's answer to large-scale HPC and AI training workloads, positioned as a previous-generation alternative to NVIDIA's Hopper-based offerings. The GPU is designed in an OAM module form factor for high-density server deployments. The MI250 delivers 362.1 TFLOPs of FP16 performance and features 128 GB of HBM2e memory with 3.2 TB/s of memory bandwidth. Its dual-GPU design provides 45.3 TFLOPs of FP64 performance per GPU die, making it particularly suited for scientific computing applications that require double-precision calculations. The accelerator includes 8 Infinity Fabric Links providing 100 GB/s of interconnect bandwidth and supports PCIe 4.0 x16 connectivity. In cloud environments, the MI250 typically serves large-scale AI training workflows, scientific simulations, and HPC applications that can leverage AMD's ROCm ecosystem. Its substantial memory capacity and high-bandwidth memory subsystem make it suitable for training large language models and handling datasets that exceed the memory limits of consumer-grade GPUs.

Common Use Cases

The MI250 is designed for large-scale AI training and high-performance computing workloads that require substantial memory capacity and computational throughput. Its 128 GB memory makes it suitable for training large language models, computer vision models with high-resolution datasets, and scientific simulations that process large amounts of data. The high FP64 performance makes it particularly valuable for scientific computing applications in computational fluid dynamics, molecular dynamics, and weather modeling. Organizations using AMD's ROCm ecosystem or seeking alternatives to NVIDIA's platform will find the MI250 effective for distributed training across multiple nodes.

Full Specifications

Hardware

Manufacturer
AMD
Architecture
CDNA 2
TDP
500W

Memory & Performance

VRAM
128GB
Memory Bandwidth
3276 GB/s
FP16
362 TFLOPS
FP64
45.26 TFLOPS
Release
2021

Frequently Asked Questions

How much does a MI250 cost per hour in the cloud?

MI250 pricing varies by provider, region, and commitment level. Check the pricing table above for current rates across all providers.

What is the MI250 best used for?

The MI250 is best suited for large-scale AI training, scientific computing, and HPC workloads that require substantial memory capacity. Its 128 GB HBM2e memory and high FP64 performance make it particularly effective for training large language models and running scientific simulations.

How does the MI250 compare to NVIDIA's H100 for AI training?

The MI250 offers 128 GB of memory compared to the H100's 80 GB, providing advantages for memory-intensive workloads. However, the H100's Transformer Engine and newer Hopper architecture generally deliver better performance per watt for modern AI training. The choice often depends on memory requirements and software ecosystem preferences between ROCm and CUDA.