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The AMD Instinct MI300X represents AMD's flagship AI accelerator, featuring an unprecedented 192GB of HBM3 memory across 8 stacks. Built on the CDNA 3 architecture with advanced chiplet design, the MI300X competes directly with NVIDIA H100 for large language model training and inference. For GPU developers, the MI300X offers an alternative to NVIDIA's CUDA ecosystem through AMD's ROCm platform. While requiring code adaptation from CUDA, many major frameworks including PyTorch and TensorFlow now support MI300X. The massive 192GB memory capacity can run larger models per GPU than any NVIDIA offering. This guide covers the MI300X's specifications, ROCm development, benchmark comparisons, and practical considerations for AMD GPU computing.
| Architecture | CDNA 3 |
| CUDA Cores | 19,456 |
| Tensor Cores | 1216 |
| Memory | 192GB HBM3 |
| Memory Bandwidth | 5,300 GB/s |
| Base / Boost Clock | 1900 / 2100 MHz |
| FP32 Performance | 81.7 TFLOPS |
| FP16 Performance | 1307 TFLOPS |
| L2 Cache | 256MB |
| TDP | 750W |
| NVLink | No |
| MSRP | $15,000 |
| Release | December 2023 |
This code snippet shows how to detect your MI300X, check available memory, and configure optimal settings for the CDNA 3 architecture.
import torch
import pynvml
# Check if MI300X is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# MI300X Memory: 192GB - Optimal batch sizes
# Architecture: CDNA 3
# CUDA Cores: 19,456
# Memory-efficient training for MI300X
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for CDNA 3
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 / 192 GB total")
# Recommended batch size calculation for MI300X
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (192 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for MI300X: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| LLaMA-70B Inference (tokens/sec) | 2,800 | Competitive with H100 |
| GPT-3 Training Throughput | 95% of H100 | Close to H100 |
| Falcon-180B Single GPU | Fits in memory | H100 requires 3+ GPUs |
| Memory Bandwidth (TB/s) | 5.1 | 96% efficiency |
| FP16 Matrix TFLOPS | 1,300 | Comparable to H100 |
| Price/Performance | 1.3x H100 | Better value |
| Use Case | Rating | Notes |
|---|---|---|
| Large Language Models | Excellent | 192GB fits 70B+ on single GPU |
| LLM Training | Good | Competitive with H100, ROCm maturing |
| LLM Inference | Excellent | Massive memory reduces sharding |
| Scientific Computing | Good | Strong FP64, HPC focus |
| Production Deployment | Good | Ecosystem growing rapidly |
| CUDA-dependent Workloads | Fair | Requires code porting |
Not directly. AMD provides hipify tool to convert CUDA to HIP (AMD equivalent). Many programs can be ported with minimal changes. Major frameworks like PyTorch have native MI300X support.
MI300X has 192GB vs H100s 80GB memory, and 5.3 TB/s vs 3.35 TB/s bandwidth. Compute is similar. MI300X excels at memory-bound LLM workloads. H100 has more mature software ecosystem.
Yes, major companies are deploying MI300X for LLM inference. PyTorch and TensorFlow support is solid. Some edge cases may have issues. The ecosystem is rapidly maturing.
PyTorch, TensorFlow, JAX, and major ML frameworks support MI300X through ROCm. vLLM, TensorRT-LLM alternatives, and inference servers are adding support.
80GB, mature CUDA ecosystem
141GB HBM3e, Hopper architecture
Previous gen, 128GB
Next gen Blackwell
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