ultraData Center

MI250X GPU

The AMD Instinct MI250X is a dual-GCD accelerator with 128GB HBM2e memory, designed for HPC and AI workloads in exascale systems.

VRAM 128GB
TDP 560W
Contact providers for pricing
MI250X GPU

Cloud Pricing

No pricing data available for this GPU at the moment.

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

Performance

FP16
383 TFLOPS
Bandwidth
3276 GB/s

Strengths & Limitations

  • 128 GB HBM2e memory capacity accommodates large datasets and models
  • 3.2 TB/s memory bandwidth supports memory-intensive workloads
  • 383 TFLOPs FP16 performance for AI training applications
  • 95.7 TFLOPs FP64 matrix performance for scientific computing
  • 8 Infinity Fabric links enable high-bandwidth multi-GPU scaling
  • OAM form factor optimized for datacenter deployment
  • ECC memory support for error-critical applications
  • 560W peak power consumption requires substantial cooling infrastructure
  • OAM form factor limits compatibility to specific server platforms
  • CDNA 2 architecture predates newer GPU generations with enhanced features
  • Limited ecosystem compared to CUDA for certain AI frameworks
  • High memory capacity may be excessive for smaller workloads

Key Features

AMD CDNA 2 Architecture
128 GB HBM2e Memory with ECC
AMD Infinity Fabric Links
AMD ROCm Software Stack
PCIe 4.0 x16 Interface
OAM Module Form Factor
Mixed Precision Compute Support
Passive Cooling Design

About MI250X

The AMD MI250X is a high-performance datacenter accelerator built on the CDNA 2 architecture and manufactured using TSMC's 6nm FinFET process. Positioned as AMD's premium compute GPU from the MI200 series, the MI250X targets HPC and AI training workloads that require substantial memory capacity and computational throughput. The GPU features an OAM (OCP Accelerator Module) form factor designed for passive cooling in server environments. The MI250X delivers 128 GB of HBM2e memory with 3.2 TB/s of peak memory bandwidth, providing extensive capacity for memory-intensive computations. Performance capabilities include 383 TFLOPs of FP16 performance, 95.7 TFLOPs of FP32 matrix operations, and 47.9 TFLOPs of standard FP64 compute. The GPU incorporates 8 Infinity Fabric links with 100 GB/s peak bandwidth for multi-GPU scaling, along with PCIe 4.0 x16 connectivity. In cloud deployments, the MI250X serves workloads requiring large memory footprints and high double-precision performance, particularly in scientific computing, large-scale AI model training, and memory-bound applications where the 128 GB capacity provides advantages over GPUs with smaller memory configurations.

Common Use Cases

The MI250X excels in HPC workloads requiring substantial memory capacity and double-precision performance, including computational fluid dynamics, molecular dynamics simulations, and climate modeling. The 128 GB memory makes it suitable for training large AI models that exceed the capacity of smaller GPUs, while the high FP64 performance serves scientific applications demanding numerical precision. Multi-GPU configurations benefit from Infinity Fabric connectivity for applications requiring distributed memory across multiple accelerators.

Full Specifications

Hardware

Manufacturer
AMD
Architecture
CDNA 2
TDP
560W

Memory & Performance

VRAM
128GB
Memory Bandwidth
3276 GB/s
FP16
383 TFLOPS
FP64
47.87 TFLOPS
Release
2021

Frequently Asked Questions

How much does a MI250X cost per hour in the cloud?

MI250X pricing varies by provider, region, and commitment level. Check the pricing table above for current rates across all providers.

What is the MI250X best used for?

The MI250X is optimized for HPC workloads requiring large memory capacity and high double-precision performance, large-scale AI model training, scientific simulations, and memory-intensive applications that benefit from the 128 GB HBM2e capacity.

How does the MI250X compare to NVIDIA H100 for AI workloads?

The MI250X offers 128 GB memory compared to H100's 80 GB, providing advantages for memory-bound workloads. However, the H100 features newer Hopper architecture with Transformer Engine and higher FP16 throughput. The choice depends on whether memory capacity or compute efficiency is the primary requirement.