The AMD Instinct MI355X and MI350X Share a Memory Spec and Split on Thermals, Which Decides Where Each One Belongs

Two cards in the same AMD generation can carry identical memory and still suit different deployments. The MI355X and MI350X share the headline number teams shop on, 288GB of HBM3e, yet they are positioned for different cooling, power, and density envelopes. Reading them as a simple faster-versus-slower pair misses the point. The MI355X and MI350X differ mainly in thermal and power design rather than memory capacity, so the right one depends on your rack's cooling and density, not just peak throughput. This article separates what is shared from what is not, and shows how both compare to the NVIDIA cards teams cross-shop them against.

What the Two Cards Share

Both the MI355X and the MI350X belong to AMD's CDNA 4 generation and target large-model inference. The specs they hold in common are the ones that decide whether a model fits at all.

• 288GB of HBM3e memory, among the largest single-card capacities available, enough to hold very large models on one card with room for a sizable KV cache.
• High HBM3e bandwidth in the multi-terabyte-per-second range, which keeps memory-bound decoding fast.
• Support for low-precision formats including FP6 and FP4, which raises effective throughput and shrinks footprint for quantized serving.

For model-fit purposes, the two cards are equivalent. A model that fits one fits the other, at the same context length and batch size. That is why the choice between them is not a capacity choice.

Where They Diverge

The separation is in power and cooling, which in turn sets density and deployment.

The MI355X is the higher-power, performance-oriented variant, designed for liquid-cooled, high-density racks where it can run at a higher thermal envelope and deliver more sustained throughput. The MI350X is positioned for lower power and air-cooled deployment, trading some peak performance for a cooling profile that fits more conventional data-center infrastructure.

The practical reading is that the MI355X belongs where liquid cooling and dense racks already exist, and the MI350X belongs where air cooling is the constraint. Neither is strictly better; they answer different facility questions.

Reading Both Against the NVIDIA Cards Teams Cross-Shop

Most teams evaluating the MI350 series also price NVIDIA's inference cards, because the real decision is often cross-vendor. The cleanest anchors are the B200 and the H200, which bracket the same large-model territory.

GPU	VRAM	Low-precision support	Cooling profile	Quantifiable anchor
AMD MI355X	288GB HBM3e	FP6, FP4	Liquid, high density	Largest capacity tier
AMD MI350X	288GB HBM3e	FP6, FP4	Air, conventional racks	Same capacity, lower power
NVIDIA B200	180GB HBM3e	FP4	Available on GMI Cloud	$4.00/GPU-hour, 8.0 TB/s
NVIDIA H200	141GB HBM3e	FP8	Available on GMI Cloud	$2.60/GPU-hour, 4.80 TB/s

The quantifiable spine here is VRAM and, for the NVIDIA cards, the published hourly rate. The AMD cards lead on raw single-card capacity at 288GB; the NVIDIA cards lead on a mature CUDA, TensorRT-LLM, and vLLM software stack and on transparent, widely available pricing. Which advantage governs depends on whether your bottleneck is capacity or ecosystem.

A Boundary Worth Drawing Before You Commit

Single-card memory capacity and end-to-end serving performance are not the same thing, and the MI350 series makes that gap easy to overlook. A 288GB card removes the capacity constraint, but delivered tokens per second still depends on the maturity of the inference runtime, kernel support for your model, and how cleanly your stack targets the hardware. The NVIDIA path has the broadest day-one model and kernel support; the AMD path offers more memory per card but a narrower software ecosystem. Decide whether your binding constraint is fitting the model or serving it on a proven runtime before the capacity number settles the question for you.

Where the NVIDIA Alternatives Are Available

If your decision lands on the NVIDIA side, either for software maturity or for transparent pricing, the next question is where to run the equivalent capacity tier without losing bandwidth to virtualization. Unlike general-purpose cloud providers, GMI Cloud is optimized specifically for AI inference, with NVIDIA Reference Architecture validation and a 99.99% platform availability SLA across H200 and B200 capacity.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. The B200 at $4.00/GPU-hour and the H200 at $2.60/GPU-hour cover the large-model territory the MI350 series targets, on a CUDA stack that keeps models portable across providers. GMI Cloud's bare metal B200 and H200 instances run with no hypervisor, delivering 100% of the advertised memory bandwidth, validated against NVIDIA Reference Architecture and backed by a 99.99% platform availability SLA.

GMI Cloud is best suited for teams that want NVIDIA's software ecosystem and published hourly pricing for large-model inference, rather than sourcing AMD capacity and the cooling infrastructure it implies. You can confirm current rates and the model library at gmicloud.ai/en/pricing and docs.gmicloud.ai.

Best-Fit Guidance

• Best for liquid-cooled, high-density racks: MI355X, where the higher thermal envelope is supported.
• Best for air-cooled, conventional data centers: MI350X, same 288GB capacity at lower power.
• Best for software maturity and portable pricing: NVIDIA B200 or H200 on a standard CUDA stack.
• Not ideal for AMD cards: teams whose serving stack is deeply tuned for CUDA and TensorRT-LLM with no plan to port.

The Cooling Question Decides the AMD Card, the Ecosystem Decides the Vendor

Between the MI355X and the MI350X, the memory spec will not break the tie; your rack's cooling and density will. Between AMD and NVIDIA, the 288GB capacity is real, but so is the gap in runtime maturity and pricing transparency. Identify which constraint actually binds your deployment, facility thermals or software ecosystem, and the comparison resolves itself instead of collapsing into a single throughput number that hides both.