AMD Data Center GPUs Explained: MI250X, MI300X, MI350X and Beyond

NVIDIA has long been the default choice for GPUs in AI. Still, AMD is working to carve out space in high-performance computing (HPC) and enterprise AI. For teams exploring alternatives to NVIDIA, the AMD MI-series GPU accelerators offer strong hardware capabilities and impressive memory bandwidth.

The main challenge lies in the AMD ecosystem. The NVIDIA CUDA platform remains the industry standard, while AMD’s ROCm software stack is still maturing. That said, with the launch of the MI300X and other CDNA-based GPUs, AMD has become a serious contender worth evaluating.

In this post, we’ll break down the GPU lineups of AMD, focusing on data center GPUs, and then compare models from the MI100 to the MI300X for different AI use cases.

AMD GPU lineup#

Similar to NVIDIA, AMD organizes GPUs into three main categories:

Radeon (Consumer/Gaming)#

Radeon GPUs are designed for gaming, graphics, and general-purpose computing. Some developers also experiment with them for AI models, but they’re not suitable for large-scale inference or enterprise workloads. Recent models include the Radeon RX 9070 XT, RX 9070, and RX 9060 XT.

Radeon Pro (Professional)#

Radeon Pro GPUs target professional creators, engineers, developers and AI professionals. They power workloads like 3D rendering, video editing, and CAD. While they are not typically deployed in big data centers, they can be used for small-scale AI inference workloads or workstation environments. Current products include the Radeon Pro R9000 series (32 GB of memory; RDNA 4) and W7000 series (up to 48 GB of memory; RDNA 3).

Instinct MI-Series (AMD Data Center GPUs)#

The Instinct MI-series is AMD’s line of GPUs built for enterprise-scale AI, HPC, and cloud computing. AMD builds its data center GPUs on a dedicated architecture called CDNA (Compute DNA). Unlike RDNA, which powers gaming and workstation cards, CDNA targets HPC and AI workloads. The focus is on raw compute performance, memory bandwidth, and scaling across multiple accelerators rather than graphics rendering.

Notable products include the MI210, MI250, MI300X and MI325X. The latest MI350 series (MI350X, MI355X), launched in June 2025, features 288 GB of HBM3E memory and offers day-zero support for major AI frameworks, libraries, and cutting-edge models.

In short: Radeon is for gaming, Radeon Pro is for professional creators, and the Instinct MI-series is AMD’s answer to NVIDIA data center GPUs.

What is AMD ROCm?#

One of the biggest differences between AMD and NVIDIA isn’t just the hardware; it’s the software ecosystem. NVIDIA has CUDA, which has become the industry standard for GPU programming. AMD’s answer is ROCm (Radeon Open Compute Platform).

ROCm is an open-source software stack that supports the CDNA architecture. It provides drivers, libraries, and tools that enable developers to run HPC and AI workloads on AMD GPUs. With ROCm, frameworks like PyTorch and TensorFlow can use AMD hardware. This is similar to how they work with CUDA.

While ROCm has matured significantly, it still trails CUDA in ecosystem size and polish. As mentioned previously, this gap has been one of the biggest challenges for AMD in competing with NVIDIA.

AMD data center GPU naming conventions#

The naming system for AMD data center GPUs is more straightforward than NVIDIA’s.

• Prefix: Instead of letters tied to architectures (like A100 for Ampere), AMD uses the MI prefix, short for Machine Intelligence.

• Numbers: The number following “MI” indicates the generation:

  • MI100 → 1st generation (CDNA)
  • MI200 → 2nd generation (CDNA 2)
  • MI300 → 3rd generation (CDNA 3)
  • MI350 → 4th generation (CDNA 4)

  Within each generation, higher model numbers (like MI250 vs. MI210) generally mean more powerful hardware, additional memory, or a different performance tier.

• Suffix: AMD often adds suffixes like X (e.g., MI250X, MI300X, MI350X) to indicate higher-performance versions with more memory or computing power than the base model.

AMD GPU comparison: MI250X vs. MI300X vs. MI325X vs. MI350X#

Here are some of the most popular AMD data center GPUs for AI inference:

GPU	Architecture	Memory	Peak Memory Bandwidth	Best For
MI100	CDNA	32 GB HBM2	1.2 TB/s	Entry-level HPC
MI250X	CDNA 2	128 GB HBM2e	3.2 TB/s	Mid-range AI inference
MI300X	CDNA 3	192 GB HBM3	5.3 TB/s	High-performance LLM training & inference, HPC
MI300A	CDNA 3	128 GB HBM3 (unified)	5.3 TB/s	Integrated AI/HPC workloads
MI325X	CDNA 3	256 GB HBM3E	6 TB/s	Advanced LLM training & inference, FP8
MI350X	CDNA 4	288 GB HBM3E	8 TB/s	Ultra-large models, long-context inference, next-gen AI

 

In practice, availability and software support (via ROCm) will play a big role in determining which GPU is the best fit for your team.

How much do AMD MI-Series GPUs cost?#

You can find AMD GPUs on several GPU cloud providers and public cloud platforms. However, not all providers carry the full range of AMD data center GPUs, and pricing can differ significantly depending on region, provider, and commitment length.

Here is the latest pricing information for the AMD MI325X and MI300X GPUs:

Provider	GPU	Billing Model	Price
Vultr	AMD MI325X	On-demand	$4.615/GPU/hr
Vultr	AMD MI325X	36-month prepaid	$2.00/GPU/hr
Vultr	AMD MI300X	On-demand	$3.99/GPU/hr
Vultr	AMD MI300X	24-month prepaid	$1.85/GPU/hr
Oracle Cloud	AMD MI300X	On-demand	$6.00/GPU/hr
DigitalOcean	AMD MI300X	On-demand	$1.99/GPU/hr
DigitalOcean	AMD MI300X	12-month commitment	$1.49/GPU/hr
DigitalOcean	AMD MI325X	12-month commitment	$1.69/GPU/hr

 

Note: Pricing data was collected from Vultr, Oracle Cloud, and Digital Ocean on September 4, 2025, and may change over time.

Unlike NVIDIA T4, A100, or H100 GPUs, standardized on-demand pricing for AMD GPUs is harder to find. Still, the MI300X and MI350 series are widely seen as direct competitors to NVIDIA H100 and H200. They often offer a lower cost per unit of memory bandwidth. For enterprise buyers, the total cost of ownership (TCO) may be more favorable with AMD, especially for long-term workloads that benefit from large HBM3/HBM3E memory pools.

 

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Now let’s take a look at some of the most common questions about AMD data center GPUs.

What is the best AMD GPU?#

There’s no single “best” AMD GPU. It depends on your workload. For smaller-scale HPC or AI inference, the MI210 or MI250 may be sufficient. For large-scale training and long-context LLM inference, the MI300X (192 GB HBM3) and the MI350X (288 GB HBM3E) are AMD’s top-end options. The best AMD GPU is the one that balances performance, memory, and cost for your specific use case.

Is AMD better than NVIDIA?#

Whether AMD or NVIDIA is “better” depends on your priorities:

• NVIDIA GPUs (like the A100, H100, and H200) dominate in software support, thanks to the CUDA ecosystem and deep framework integration.
• AMD GPUs (MI300X, MI350X) often provide more memory at a lower cost per GPU. This makes them attractive for memory-bound AI workloads such as LLMs with long context windows.

In practice, NVIDIA is still the most widely adopted in AI, but AMD is increasingly competitive in HPC and enterprise AI inference.

MI300X vs. H100#

The AMD Instinct MI300X and NVIDIA H100 are often compared because they target similar high-performance AI workloads:

• Memory: MI300X offers 192 GB HBM3, while H100 provides 80 GB HBM2e (with the H200 extending this to 141 GB). This makes MI300X particularly strong for long-context LLMs.
• Ecosystem: H100 has the advantage of CUDA and broad adoption across cloud providers. MI300X relies on ROCm, which has matured but is still catching up in ecosystem support.
• Performance: Benchmarks vary by workload. However, the MI300X is a strong competitor, especially for memory-heavy AI tasks.

Before choosing a GPU for AI inference in production, it’s essential to benchmark against your own workloads and goals. This is especially important for optimization with advanced distributed inference techniques such as prefix caching, KV cache offloading, or prefill–decode disaggregation.

Conclusion#

AMD is no longer just a challenger in the GPU market. It’s becoming an essential part of the conversation for AI infrastructure. For teams limited by the supply or pricing of NVIDIA GPUs, the AMD MI300X and MI350 series provide a credible and cost-effective alternative.

The real question isn’t “AMD or NVIDIA?” Instead, it is how to build an inference stack that can flexibly adapt to changing models, frameworks, and optimization techniques. Whether or not this inference layer can help you smoothly run workloads with these new technologies may be the most strategic choice of all.

At Bento, our unified inference platform helps enterprises do exactly that. AI teams can seamlessly tap into the most suitable NVIDIA or AMD GPUs at the best rates and availability. Also, they can apply custom inference optimizations to get optimal performance with speed and ease for their unique use cases.

Learn more:

• Read our LLM Inference Handbook for practical optimization strategies
• Join our Slack community to connect with other AI practitioners
• Schedule a call with our experts if you need tailored advice on GPU inference