H100 vs A100 vs MI300X: The GPU Wars

For years, the answer to “Which GPU for AI?” was simply “The best NVIDIA card you can afford.” But with the release of AMD’s Instinct MI300X and NVIDIA’s H100, the landscape has shifted into a genuine heavyweight boxing match.

Let’s break down the specs, the performance, and the reality of the three most important chips in the data center.

1. NVIDIA A100 (The Workhorse)

Released: 2020

The A100 is the chip that built ChatGPT. Even in 2025, it remains the backbone of most inference fleets and university clusters.

• Memory: 80GB HBM2e
• Bandwidth: 2.0 TB/s
• FP16 Performance: 312 TFLOPS
• Interconnect: NVLink (600 GB/s)

Verdict: Still excellent, but showing its age. It lacks the Transformer Engine and FP8 support, making it inefficient for the newest massive models compared to its successor.

2. NVIDIA H100 (The Gold Standard)

Released: 2022

The H100 “Hopper” is the current currency of the AI realm. It introduced the Transformer Engine, which intelligently manages precision (FP8) to speed up Transformer models specifically.

• Memory: 80GB HBM3
• Bandwidth: 3.35 TB/s
• FP8 Performance: ~4,000 TFLOPS (with sparsity)
• Interconnect: NVLink (900 GB/s)

Verdict: The undisputed king of training. The software support (CUDA) and the NVLink ability to chain 256 GPUs into a single “super-GPU” make it the default choice for training LLMs.

3. AMD Instinct MI300X (The Challenger)

Released: 2023

AMD didn’t just try to match the H100; they tried to beat it on raw specs. The MI300X is a “chiplet” design—a monster of stacked silicon.

• Memory: 192GB HBM3
• Bandwidth: 5.3 TB/s
• Performance: Competitive with H100 in raw FLOPS.

The Killer Feature: VRAM The MI300X has 192GB of memory vs the H100’s 80GB. This is a game changer for Inference.

• A Llama-3-70B model fits comfortably on one MI300X.
• It requires two H100s.

For inference providers, the MI300X offers a massive cost advantage. You buy fewer cards to serve the same model.

The Software Gap: CUDA vs ROCm

If the MI300X is so good, why does NVIDIA still own 90% of the market? Software.

NVIDIA’s stack just works. AMD’s ROCm stack has historically been buggy and hard to install. However, this is changing rapidly. Frameworks like vLLM and Hugging Face TGI now support AMD out of the box. For pure inference workloads, the “CUDA Moat” is drying up.

Comparison Chart

Feature	NVIDIA A100	NVIDIA H100	AMD MI300X
VRAM	80 GB	80 GB	192 GB
Bandwidth	2 TB/s	3.35 TB/s	5.3 TB/s
FP8 Support	No	Yes	Yes
Training	Good	Best	Good
Inference	Good	Great	Best Value
Price	~$15k	~$30k	~$20k

Conclusion

• Training a Foundation Model? Buy H100s. You need the reliability and NVLink scale.
• Running Inference? Look seriously at MI300X. The memory capacity allows you to run bigger models on fewer cards, slashing your OpEx.
• Budget Constrained? Pick up used A100s. They are still incredibly capable.