AMD MI300X vs NVIDIA H200 Comparison | Generated by AI

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Question: How does AMD MI300X compare to NVIDIA H200?

Answer:## Specs: MI300X vs H200

Metric AMD MI300X NVIDIA H200 Winner
Architecture CDNA 3 Hopper —
VRAM 192 GB HBM3 141 GB HBM3e AMD
Memory BW 5.3 TB/s 4.8 TB/s AMD
FP32 TFLOPS 163 67 AMD
FP16 TFLOPS 326 134 (989 w/ sparsity) AMD raw / NVIDIA real
INT8 TOPS ~2,600 2,680 NVIDIA
TDP 750W 700W NVIDIA
Price (cloud) ~$2.50/hr ~$2.25/hr NVIDIA

Raw specs favor AMD on paper heavily. Reality is more nuanced.

Real-World Benchmark Numbers

Inference (LLM throughput)

In multi-GPU vLLM benchmarks using Llama 3.1 8B, MI300X achieves ~18,752 tokens/second on a single GPU — roughly 74% of H200’s throughput. Scaling efficiency is 95% at 2 GPUs but drops to 81% at 4 GPUs.

At lower concurrencies (under ~250 concurrent queries), H200 delivers faster per-query output speeds and lower latency. But at high concurrency, MI300X flips it — higher peak system throughput and lower cost per token.

Latency

H200 consistently delivers 37–75% lower latency than MI300X across tested configurations. On DeepSeek R1, H200 hit 6,311 tokens/s offline throughput vs MI300X’s 4,574 tokens/s.

Training

SemiAnalysis’s 5-month deep dive: real-world MI300X training performance on public stable AMD software lags H100/H200 significantly. Training performance per TCO is worse on MI300X on stable releases — but this changes with custom AMD dev builds.

Clarifai’s engineers found MI300X achieves only 37–66% of H100/H200 performance due to software overhead — but can outperform H100 on memory-bound tasks, delivering up to 40% lower latency and doubling throughput for certain models.

The Real Story: Software is the Bottleneck

This is the core thesis. MI300X hardware is competitive or better. ROCm is the tax you pay:

Hardware specs:     AMD wins or ties
Real training:      AMD ~65-75% of H200 (stable ROCm)
Real inference:     AMD ~74% throughput, 37-75% worse latency
Memory-bound tasks: AMD sometimes WINS (192GB is huge)
Cost per token:     AMD wins at high concurrency

Getting MI300X training performance within 75% of H100/H200 required significant effort from SemiAnalysis — including custom Dockerfiles built from source with direct AMD engineering support.

Where MI300X Actually Wins

  1. 192GB VRAM — fit 70B models in BF16 on a single card. H200 can’t do this (141GB).
  2. High-concurrency inference — at large batch sizes, MI300X flips the cost equation
  3. Memory-bound workloads — long context windows, large KV caches
  4. Availability — H200s are scarce; MI300X stock actually exists (as you found)
  5. Cost — cheaper per hour, and AMD Developer Cloud via DigitalOcean is accessible

AMD’s Trajectory (Important for Decision-Making)

The MI325X (available Q4 2024) upgrades to 256GB HBM3e + 6TB/s bandwidth. The MI350X/MI355X (CDNA 4, TSMC 3nm, shipping 2025) delivers ~4,600 TOPS FP8 — roughly 1.8× MI300X in FP8 throughput, with near-parity to B200 on some FP8 training benchmarks.

ROCm is also improving fast. On Llama-2 70B LoRA FP8, MI355X slashed training time from ~28 minutes (MI300X) to just over 10 minutes — a 2.8× speedup, with results within ~10% of B200/GB200 submissions.

Decision Matrix for You

Use Case Pick
LoRA fine-tuning (HF stack) MI300X ✅ (works, cheaper)
nanoGPT-scale training MI300X ✅ (PyTorch ROCm is fine)
Custom CUDA kernels H200 (don’t touch ROCm)
70B model inference MI300X wins (192GB fits it)
Low-latency serving H200
High-throughput batch inference MI300X at scale
Availability right now MI300X wins

Bottom Line

The “70%” figure from before still roughly holds for inference throughput vs H200 specifically (~74%). For training it can be worse (37–66%) or close to parity depending on ROCm version and workload. The 192GB VRAM advantage is real and matters for large model work. For your use case — LoRA fine-tuning, agent tooling, model training at nanoGPT scale — MI300X via AMD Developer Cloud is a solid pragmatic choice, especially given H200 scarcity.

References:

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