H100 SXM vs MI300X

MI300X has 2.4× the memory; MI300X has 1.6× the bandwidth.

The NVIDIA H100 SXM (Hopper, 2022) and the AMD Instinct MI300X (CDNA 3, 2023) come from different vendors and different design goals, but they get cross-shopped for a reason. On capacity, the MI300X carries 2.4x the memory (192 vs 80 GB), which sets what each can hold at all. On memory bandwidth, the number that governs LLM serving speed, the MI300X leads at 5.3 vs 3.35 TB/s.

Price is where it settles: the MI300X rents from $1.85/hr against $1.90/hr for the H100 SXM, close enough that price should not decide. Per gigabyte of VRAM, the MI300X is the cheaper rental, worth knowing if your model is capacity-bound rather than speed-bound. For how these numbers translate into tokens per dollar, the inference cost estimator runs both cards against any model. Everything below is the underlying data.

NVIDIA · Hopper · 2022

NVIDIA H100 SXM

The workhorse of the AI boom. Still the most widely available serious training and inference GPU.

From $1.90/hr at Hyperstack

AMD · CDNA 3 · 2023

AMD Instinct MI300X

AMD's answer to Hopper: 192 GB on one GPU, a 70B model in FP16 fits with room to spare.

From $1.85/hr at Vast.ai

Head to head

■ H100 SXM   ■ MI300X, bars share one scale across the whole catalog.

Memory 80 GB 192 GB
Memory bandwidth 3.35 TB/s 5.3 TB/s
FP16 dense 990 TFLOPS 1,307 TFLOPS
FP8 dense 1,979 TFLOPS 2,614 TFLOPS
Power (TDP) 700 W 750 W
H100 SXMMI300X
Memory80 GB HBM3192 GB HBM3
Bandwidth3.35 TB/s5.3 TB/s
FP16 dense990 TF1,307 TF
FP8 dense1,979 TF2,614 TF
TDP700 W750 W
InterconnectNVLink 4 · 900 GB/sInfinity Fabric · 896 GB/s
Cheapest rental $1.90/hr $1.85/hr
$/hr per GB VRAM $2.4¢ $1.0¢

What one can run that the other can't

Only on H100 SXM

Nothing, MI300X runs everything H100 SXM does.

Only on MI300X

  • Mixtral 8x22B (8-bit)
  • gpt-oss-120b (8-bit)

Rule of thumb: for LLM serving, prefer the GPU with more memory bandwidth per dollar; for training and prefill-heavy work, prefer FLOPS per dollar; and if the model doesn't fit in VRAM, none of the other numbers matter. Sanity-check with the VRAM calculator.