Comparison

NVIDIA H100 vs AMD MI300X

NVIDIA vs AMD — the cross-vendor showdown

AMD's MI300X offers 31% more FP16 TFLOPS (1,300 vs 990) and 140% more memory (192GB vs 80GB) than the H100, but the H100 benefits from the more mature CUDA ecosystem.

Pricing Comparison

Specifications

Specification	NVIDIA H100	AMD MI300X
Manufacturer	NVIDIA	AMD
Architecture	Hopper	CDNA 3
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	80 GB	192 GB
FP16 Performance	990 TFLOPS	1300 TFLOPS
TDP	700W	750W
Perf per Watt	1.41 TFLOPS/W	1.73 TFLOPS/W

Detailed Analysis

The H100 vs MI300X comparison represents the most significant cross-vendor GPU competition in the AI accelerator market. AMD's CDNA 3-based MI300X competes aggressively on specifications, offering 1,300 FP16 TFLOPS and 192GB of HBM3 memory — substantially more than the H100's 990 TFLOPS and 80GB HBM3.

The MI300X's 5.3 TB/s memory bandwidth also exceeds the H100's 3.35 TB/s, giving it a significant advantage on memory-bandwidth-bound workloads like large model inference.

However, the H100's advantage lies in its ecosystem. NVIDIA's CUDA platform, cuDNN libraries, and extensive framework optimisation mean that most AI workloads run efficiently on H100 out of the box. The MI300X uses AMD's ROCm software stack, which has improved significantly but may require additional effort to optimise certain workloads.

In the cloud market, MI300X availability is growing as providers diversify their offerings. Pricing is generally competitive with or below H100, making it an attractive option for cost-sensitive deployments, especially for organisations willing to invest in ROCm optimisation.

Verdict

Best for Training

H100 for maximum ecosystem compatibility. MI300X when memory capacity is critical and the team can optimise for ROCm.

Best for Inference

MI300X's 192GB memory advantage can serve larger models on fewer GPUs, potentially reducing total cost.

Best Value

MI300X often offers better raw specs per dollar. Factor in software ecosystem costs when comparing.

Frequently Asked Questions

Is the MI300X better than the H100?

On specifications, the MI300X has more TFLOPS (1,300 vs 990) and more memory (192GB vs 80GB). On ecosystem maturity, the H100 wins with CUDA. The best choice depends on your workload and team's GPU platform experience.

Can I run CUDA code on the MI300X?

Not directly. The MI300X uses AMD's ROCm stack. However, tools like HIP allow porting CUDA code to ROCm, and major frameworks like PyTorch have strong ROCm support.