GPU COMPARISON

AMD MI300X vs NVIDIA H100: Can AMD Compete?

Summary • 12 Data Sources

How does AMD MI300X compare to NVIDIA H100?

AMD MI300X offers 192GB HBM3 memory (vs 80GB on H100) and 5.3 TB/s bandwidth, making it compelling for memory-bound inference. MI300X is typically 20-30% cheaper than H100. However, NVIDIA's CUDA ecosystem remains dominant—most ML frameworks work better with CUDA. Choose MI300X for cost-sensitive inference; stick with H100 for training and production workloads.

Key Data Points

GPU Memory: 192GB vs 80GB (+140%)
Memory Bandwidth: 5.3 TB/s vs 3.35 TB/s (+58%)
Lease Rates: $1.80-$2.50/hr vs $2.50-$3.50/hr (20-30% less)
Software: ROCm (AMD) vs CUDA (NVIDIA)
Inference: MI300X +29% tokens/sec on Llama 2 70B

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Head-to-Head Specifications

Specification	AMD MI300X	NVIDIA H100 SXM	Winner
GPU Memory	192 GB HBM3	80 GB HBM3	MI300X (+140%)
Memory Bandwidth	5.3 TB/s	3.35 TB/s	MI300X (+58%)
FP16 Performance	1,307 TFLOPS	1,979 TFLOPS	H100 (+51%)
FP8 Performance	2,614 TFLOPS	3,958 TFLOPS	H100 (+51%)
TDP	750W	700W	H100 (7% less)
On-Demand Lease Rate	$1.80 - $2.50/hr	$2.50 - $3.50/hr	MI300X (20-30% less)
Software Ecosystem	ROCm (improving)	CUDA (dominant)	H100
Availability	Improving	Good	H100

Software Ecosystem: CUDA vs ROCm

NVIDIA CUDA

•15+ years of ecosystem development
•Native support in PyTorch, TensorFlow, JAX
•Extensive library support (cuDNN, cuBLAS, NCCL)
•Most production ML code assumes CUDA
•Best debugging and profiling tools

AMD ROCm

•Rapidly improving (ROCm 6.0+)
•PyTorch support is now stable
•Some CUDA code ports with hipify
•Limited third-party library support
•Smaller community, less documentation

When to Choose Each GPU

Choose MI300X When:

•Memory-bound inference (large models, long contexts)
•Cost optimization is critical (20-30% savings)
•Running Llama 70B on fewer GPUs
•Team has ROCm experience or willingness to learn
•Workload is well-tested on ROCm

Choose H100 When:

•Training workloads (CUDA optimization critical)
•Production systems requiring maximum reliability
•Using specialized libraries (FlashAttention, etc.)
•Need best-in-class support and debugging
•Existing CUDA codebase and expertise

Real-World Benchmarks

Benchmark	MI300X	H100 SXM	Ratio
Llama 2 70B Inference (tokens/sec)	~1,800	~1,400	MI300X +29%
Llama 2 7B Training (samples/sec)	~320	~450	H100 +41%
Stable Diffusion XL (images/sec)	~12	~18	H100 +50%
Long Context Inference (32K tokens)	Fits 1 GPU	Requires 2 GPUs	MI300X

Benchmarks from public MLPerf results and community testing. Actual performance varies by workload and optimization.

Frequently Asked Questions

Will ROCm catch up to CUDA?

ROCm is improving rapidly with AMD's investment post-MI300 launch. For standard PyTorch workloads, it's now usable. However, reaching CUDA parity for the full ecosystem (debugging, profiling, third-party libraries) will take years.

Can I port my CUDA code to ROCm?

AMD provides the "hipify" tool that automatically converts CUDA code to HIP (ROCm's API). Simple CUDA code ports well, but complex kernels and library dependencies often require manual work.

Is MI300X available in the cloud?

Yes, cloud providers like Microsoft Azure and several GPU cloud providers now offer MI300X instances. Availability is growing but still limited compared to H100. Check for current pricing and availability.

What about AMD MI350 vs NVIDIA B200?

AMD's MI350 (expected 2026-2027) will compete with NVIDIA B200. Both promise significant improvements over current generation. It's too early to compare, but AMD is committed to closing the gap.