The AMD MI300X as an alternative to NVIDIA

Memory first, software second, price third

The AMD MI300X is an Instinct accelerator with 192 GB of memory, against 80 GB on an H100. That memory lets one card hold what takes 2 or 3 H100s: a 60 billion parameter model in 16-bit, at about 120 GB, fits with room. The question that decides whether it is an alternative for you is software. Your serving engine, framework and custom kernels must run on AMD's stack, and that has to be tested on your code before the rate means anything.

This site holds no benchmarks for the MI300X or the H100, and no figures on how either performs on any model. It holds memory sizes and listed rates, and the rest of this post is built from those and from the questions worth asking.

That makes the MI300X an unusual comparison. Most GPU choices are between NVIDIA cards that share a software stack, so the only variables are memory, rate and terms. Here the stack itself changes, and that is the variable most likely to decide it.

What 192 GB holds

At 2 bytes a parameter, 192 GB of weights is about 96 billion parameters, so a single card serves models up to roughly that size in 16-bit, minus whatever the cache needs. In 8-bit it holds weights for a model of about 190 billion parameters, again before the cache. For a 60 billion parameter model in 16-bit the spare memory is around 72 GB, room for longer contexts and more simultaneous users.

On H100s the same 16-bit 60 billion parameter model needs at least 2 cards and a split. That split costs setup time, traffic between cards, and a second hourly rate. The MI300X avoids it, if the software works.

The MI325X, a later AMD part, carries 256 GB. This site holds no listed rate for it, so this post leaves it unpriced. Even so, the memory gap with 80 GB cards widens further up AMD's range.

The software question, in practice

NVIDIA cards run CUDA, and a great deal of machine learning code was written for it first. AMD cards run ROCm. The main frameworks and several serving engines support ROCm, but support for a given model, quantisation method, attention kernel or library version can differ. A project that pins an old version or uses a custom CUDA kernel is the one most likely to hit a wall.

List the pieces your workload depends on: framework and version, serving engine and version, quantisation format, any custom extensions. Check each against the project's own documentation for AMD support. Then rent an MI300X for a day and run the real thing. A failure on day one is cheap; a failure after a migration is not.

Ask the provider which ROCm version the image ships with, and whether you can bring your own container. That decides how much of the checking you control.

Rates and sellers

Cheapest hourly listing on record for the MI300X 192GB: $2.39. Sellers with a listed rate: 4. Every listing is on the MI300X page with the date it was read. For comparison, the H100 SXM page reads $1.68 (sellers with a listed rate: 13), and the B200, the NVIDIA card with the same 192 GB, reads $3.74.

Divide each rate by the card's memory for a cost per gigabyte per hour. When memory is what you are buying, that is the first fair comparison. Speed is a separate question, and the answer comes from your own test.

What a move costs beyond the rate

Moving a working NVIDIA deployment to AMD has costs that never appear on a price page. Container images need rebuilding against ROCm. Quantised model files may need converting to a format the engine supports on AMD. Monitoring that reads NVIDIA's management tools needs an equivalent.

Each of those is engineering time, and the first run on new hardware usually surfaces something nobody listed. Count those hours at your own rate and add them to the comparison. A lower hourly rate that takes a fortnight of work to reach may still win over a year of serving; over a month it may not.

There is also the question of a second source. Some teams run the same model on both vendors so that a shortage of one card does not stop the service. That decision is about resilience, not rate, and it only makes sense once the software runs cleanly on both.

When to stay on NVIDIA

When a model fits on an 80 GB card with room to spare, the MI300X's memory is not the reason to move, and a switch of software stack is a cost with nothing to pay for it. If your code depends on CUDA-only libraries, the move is a porting project, not a rental decision.

Where neither applies and you need more than 80 GB per card, it is a reasonable candidate to put through the same test as any NVIDIA card. The B200 post lists the questions to ask a provider about a card with a thin market, and most of them apply here too.

A fair first step is cheap: one rented MI300X, one day, your real workload in a container you control. A clean run gives you a second option with more memory per card; a failure names the dependency to watch.