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The AMD Instinct MI250X powers the Frontier supercomputer and represents AMD's previous-generation flagship for HPC and AI workloads. With 128GB of HBM2e memory in a multi-die design and exceptional FP64 performance, the MI250X targets scientific computing alongside machine learning. For GPU developers, the MI250X offers an alternative to NVIDIA A100 through ROCm. Its dual-GCD design provides massive parallelism, though it requires understanding AMD's unique architecture. Major HPC applications and growing ML framework support make it viable for production workloads. This guide covers the MI250X's specifications, ROCm development, benchmark comparisons, and practical considerations for AMD GPU computing.
| Architecture | CDNA 2 |
| CUDA Cores | 14,080 |
| Tensor Cores | 880 |
| Memory | 128GB HBM2e |
| Memory Bandwidth | 3,200 GB/s |
| Base / Boost Clock | 1700 / 1900 MHz |
| FP32 Performance | 47.9 TFLOPS |
| FP16 Performance | 383 TFLOPS |
| L2 Cache | 16MB |
| TDP | 560W |
| NVLink | No |
| MSRP | $12,000 |
| Release | November 2021 |
This code snippet shows how to detect your MI250X, check available memory, and configure optimal settings for the CDNA 2 architecture.
import torch
import pynvml
# Check if MI250X is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# MI250X Memory: 128GB - Optimal batch sizes
# Architecture: CDNA 2
# CUDA Cores: 14,080
# Memory-efficient training for MI250X
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for CDNA 2
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 128 GB total")
# Recommended batch size calculation for MI250X
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (128 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for MI250X: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| HPL (FP64 TFLOPS) | 42.5 | Excellent for HPC |
| ResNet-50 Training (imgs/sec) | 1,100 | Competitive with A100 |
| BERT Training Throughput | 90% of A100 | Close performance |
| Memory Bandwidth (TB/s) | 3.1 | 97% efficiency |
| FP64 Matrix TFLOPS | 47 | Best in class for era |
| Multi-GPU Scaling | 95% | Infinity Fabric efficient |
| Use Case | Rating | Notes |
|---|---|---|
| HPC/Supercomputing | Excellent | Powers Frontier #1 supercomputer |
| Scientific Computing | Excellent | Outstanding FP64 performance |
| ML Training | Good | Competitive with A100 |
| Climate Modeling | Excellent | Large memory, strong FP64 |
| ML Inference | Good | MI300X better for LLMs |
| CUDA Shops | Fair | Requires porting effort |
MI300X has 192GB vs 128GB, 5.3 vs 3.2 TB/s bandwidth, CDNA 3 vs CDNA 2, and significantly better ML performance. MI250X excels at FP64 HPC. MI300X is better for LLMs and AI.
MI250X is competitive with A100 for ML training. Its 128GB memory helps with large batch sizes. For inference and LLMs, MI300X is significantly better. ROCm support has improved substantially.
MI250X exceptional FP64 performance (47.9 TFLOPS) makes it ideal for HPC workloads. The 128GB memory and Infinity Fabric scaling enable massive simulations. Frontier demonstrates AMD competitiveness at scale.
Yes, MI250X can train LLMs with 128GB memory per GPU. However, MI300X with 192GB and better transformer performance is preferred for LLM workloads. MI250X is better suited for HPC.
192GB, much faster for AI
80GB, mature CUDA ecosystem
Next gen NVIDIA
Previous gen, widely available
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