NVIDIA H100 vs. AMD Instinct MI250X: Which AI Chip Is Better?

H100 vs. MI250X

NVIDIA H100 vs. AMD Instinct MI250X: Which AI Chip Is Better?

Key Points

  • The NVIDIA H100 and the AMD Instinct MI250X are powerful AI chips competing for dominance in the AI market.
  • The H100 offers higher performance and compatibility with a wide range of AI frameworks, but it comes at a steep price point of around $30,000.
  • The MI250X offers competitive performance, better power efficiency, and a lower price point of approximately $10,000.
  • The H100 has a thermal design power (TDP) of 350W, while the MI250X has a TDP of 500W.
  • The H100 has 80GB of memory with a bandwidth of 2039GB/s, while the MI250X has 128GB of memory with a bandwidth of 3277GB/s.

In the evolving landscape of artificial intelligence, NVIDIA and AMD stand at the forefront, pushing the boundaries of what’s possible in computing power. Both companies have released powerful AI chips, but when it comes to H100 vs. MI250X, which one is truly superior? This article will break down the performance metrics, statistics, and features of both to determine which AI chip is the best.

H100 vs. MI250X: Side-by-Side Comparison

CategoryNVIDIA H100 GPUAMD Instinct MI250X GPU
FP32/FP64 (double) Performance51.22/25.61 teraFLOPS47.87/47.87 teraFLOPS
Power EfficiencyTDP of 350WTDP of 500W
MemoryOffers 80GB at 2039GB/sOffers 128GB at 3277GB/s
CompatibilityCompatible with a wide range of AI frameworks, including TensorFlow, PyTorch, and CaffeBest used within the AMD ecosystem of products
Price Point~$30,000~$10,000
RecommendationHighly efficient and features robust compatibility at a very steep price pointOffers higher performance for a third of the cost but with worse efficiency

H100 vs. MI250X: What’s the Difference?

H100 vs. MI250X
NVIDIA is one of the most powerful chip manufacturers in the world today.

Artificial Intelligence isn’t a future technology anymore. Virtually everywhere in the world today, you can find it. Its applications weave into the very fabric of our daily lives, from recommendations on streaming platforms to advanced medical diagnoses. Underpinning this technological marvel is the bedrock of computational power provided by specialized AI chips.

Designers crafted these chips to handle the computational demands of complex machine learning algorithms effortlessly — demands that standard processors would struggle with. Their architecture and design principles stand apart from the average CPU because they process vast amounts of data simultaneously. In other words, these chips make decisions in split-seconds and run deep learning models with billions of parameters.

Against this backdrop, NVIDIA and AMD have emerged with their flagship AI chips: the NVIDIA H100 and the AMD Instinct MI250X. Each chip is a testament to years of research and innovation, with both aiming to capture the lion’s share of the AI market. However, to truly appreciate the power and nuances of these chips, one must dive into their respective histories and what they bring to the table.


NVIDIA, founded in 1993, quickly became synonymous with graphics processing units (GPUs) and gaming. However, over time, the company recognized the potential of GPUs and artificial intelligence. The parallel processing capabilities of GPUs, originally designed for rendering graphics, proved to be adept at handling the simultaneous computations required by neural networks. This realization marked the genesis of NVIDIA’s foray into the AI domain.

The NVIDIA H100 represents the pinnacle of this journey. As the latest in NVIDIA’s line of high-performance computing chips, the H100 boasts architecture advancements that propel it miles ahead of its predecessors. Leveraging technologies such as tensor cores and advanced memory management, the H100 is optimized for both training and inference tasks in deep learning.

Its vast memory pool allows it to handle massive datasets with ease. Furthermore, its compatibility with leading AI frameworks ensures that developers can harness its power without being encumbered by compatibility issues. NVIDIA’s extensive ecosystem, including platforms like CUDA and cuDNN, further accentuates the H100’s appeal to AI professionals and researchers.

The AMD Instinct MI250X GPU

H100 vs. MI250X
AMD is a world-leading semiconductor manufacturer for both family and commercial environments.

Advanced Micro Devices (AMD) has always been NVIDIA’s fierce rival, especially in the realm of graphics. Founded in 1969, AMD initially focused on CPUs but ventured into GPUs after acquiring ATI Technologies in 2006. Recognizing the same GPU potential in AI as NVIDIA did, AMD began refining its graphics cards, eventually birthing the Instinct chip line, tailored for machine learning and AI computations.

The AMD Instinct MI250X is a testament to AMD’s commitment to pushing the boundaries of AI processing. Built on advanced lithography, the MI250X combines high computational throughput with efficiency. Like its counterpart from NVIDIA, the MI250X is equipped with high bandwidth memory, ensuring rapid data retrieval during intensive tasks.

However, one area where the MI250X distinguishes itself is in its seamless integration with AMD’s proprietary tools. These tools give developers a unique blend of performance and customization. Its power efficiency metrics, combined with competitive pricing, position the MI250X as a compelling choice for institutions and professionals who need top-tier performance without breaking the bank.

Performance Benchmarks


When analyzing the performance of two state-of-the-art AI chips, it’s crucial to understand that benchmarks can offer both quantitative and qualitative insights. The raw numbers depict the sheer computational might, while the qualitative metrics reveal how these chips fare in real-world applications. With this in mind, it is no secret that the NVIDIA H100 is an absolute powerhouse.

With its latest architectural advancements and tensor cores specifically designed for AI, the H100 achieves impressive teraFLOPS benchmarks, which gives a measure of computer performance. Specifically, the NVIDIA H100 GPU reaches a performance benchmark of FP32/FP64 of 51.22 and 25.61 teraFLOPS, respectively. The terms FP32 and FP64 refer to the precision of floating-point calculations that a computing device can perform. FP32 stands for 32-bit floating-point, and FP64 stands for 64-bit floating-point.

The H100 particularly excels in mixed-precision computations with these performance marks. Such computations are essential for tasks like training large neural networks, where you can trade precision for speed without compromising the model’s accuracy. In practical scenarios, the H100 showcases its prowess in tasks that require immense parallelism.

Whether it’s training a multi-layered neural network, performing data analytics, or running simulations, the H100 delivers consistent, high-performance results. Its compatibility with NVIDIA’s robust ecosystem further ensures that software optimization is top-notch. This is accomplished by extracting every ounce of performance from the hardware.


Part of AMD Instinct Series
AMD Instinct MI100 Graphics Card
  • MI100 is part of the AMD Instinct series
  • Pcl Express 4.0 graphics card
  • Has GPU clock speed of 1502 MHz
  • Features 32G RAM size
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01/17/2024 10:40 am GMT

The AMD Instinct MI250X is a testament to AMD’s commitment to driving the AI revolution. While it might occasionally trail the H100 in certain metrics, this doesn’t paint the complete picture. Looking across a range of benchmarks, the MI250X not only holds its ground but often surpasses the H100, demonstrating its prowess in a diverse set of tasks.

One of the most impressive aspects of the MI250X is its balanced performance. It showcases an exceptional teraFLOPS benchmark for both FP32 and FP64 at 47.87 teraFLOPS each, higher than the H100. This level of performance means that whether you’re dealing with single-precision or double-precision tasks, the MI250X delivers consistent, top-tier results.

Beyond the raw numbers, it’s essential to consider the chip’s adaptability to real-world workloads. AMD has historically optimized its chips for a blend of performance and practicality. The MI250X is no exception. It is designed to handle a wide array of applications, from machine learning training to inference tasks.

Power Efficiency


As AI technologies surge forward, chip manufacturers are pushed to find a balance between unparalleled performance and power efficiency. Power consumption refers to factors like sustainability, heat dissipation, and long-term wear and tear of devices. High power consumption typically means more heat, which in turn means more cooling, more noise, and potentially shorter hardware lifespans.

NVIDIA’s H100 does come with its set of power demands, though it is far and away more efficient than AMD’s MI250X. With high-performance metrics, the chip’s consumption usually scales proportionately. However, NVIDIA has implemented several power-saving techniques, ensuring that when the chip isn’t under heavy load, it is more efficient. As for specifics, the NVIDIA H100 has a thermal design power (TDP) of 350W.

Hardware manufacturers use TDP to show how much heat a component, such as a GPU, must dissipate to operate within its temperature limits. They express this in watts (W). So the metric above shows that under typical workload conditions, designers expect the H100 to consume and dissipate up to 350W of power.


AMD’s legacy of optimizing the power-to-performance ratio comes out significantly with the MI250X. This chip is meticulously engineered to squeeze out every bit of performance for each watt it consumes. The MI250X, however, has been found to operate at a higher wattage compared to its NVIDIA counterpart.

The increased 500W power footprint of the MI250X translates to greater heat production, necessitating more stringent cooling solutions. This may become a limiting factor while attempting to keep operations within temperature limits, depending on the location. This applies to settings like a research lab, a server farm, or an AI workstation.

Memory and Speed


Rare H100 Graphics Card
NVIDIA H100 80 GB Graphic Card PCIe
  • Includes 1-year warranty
  • Built for accelerated data analytics and real-time deep learning
  • Specializes in A.I. algorithms and programs
  • 350W operating power
We earn a commission if you make a purchase, at no additional cost to you.
01/17/2024 10:40 am GMT

Memory and speed are more than just benchmarks in the realm of AI and machine learning. These metrics dictate how effectively a chip can handle vast datasets, implement intricate algorithms, and manage instantaneous computations. Plus, with complex operations, speed can be a major factor in project success. To begin, the NVIDIA H100 does stand out in terms of its sheer memory volume but is outperformed overall.

The NVIDIA H100, while impressive, is dwarfed in terms of raw memory size when compared to the MI250X. It comes with 80GB of onboard memory, geared towards accommodating expansive datasets. This memory bank proves essential in AI tasks, particularly when confronted with extensive deep learning models or neural network computations that require access to diverse data points simultaneously.

However, storage is merely one side of the coin. The speed at which a chip can read or write data to this memory makes a significant difference. With a bandwidth of 2039GB/s, the H100 ensures that data transfers are both voluminous and brisk. However, once again, the H100 falls drastically behind the MI250X.


On the flip side, the MI250X stands out with its much larger memory capacity. Housing a massive 128GB, it’s tailor-made for a diverse range of applications, ensuring it doesn’t fall short in memory-intensive tasks. This gives AMD users nearly 50 more GB of space to work with.

Additionally, AMD’s commitment to performance shines through with the MI250X’s remarkable data retrieval capabilities, thanks to a staggering bandwidth of 3277GB/s. By minimizing memory latency, the MI250X boasts access to its vast memory banks at speeds that can leave competitors in the dust. This agility becomes a determining factor in tasks requiring swift iterations or real-time processing.

Architecture and Scalability


H100 vs. MI250X
NVIDIA has taken the necessary steps to ensure scalability with its H100 GPU.

When discussing chip design, the architecture is essentially the blueprint of its internal components and how they interconnect. This is a critical aspect as it dictates how efficiently a chip processes information, manages power, and communicates with other components. Additionally, a chip’s architecture also plays a vital role in ensuring compatibility with diverse software and hardware platforms.

Scalability is another pivotal attribute, particularly in today’s fast-evolving tech environment. It refers to the chip’s innate ability to cope with escalating workloads without suffering a degradation in performance. In a world where clustered and parallel processing environments are becoming commonplace, especially in data centers and high-performance computing, scalability is indispensable.

The H100, an exemplar from NVIDIA’s prestigious line, is architected around the concept of parallel processing. Parallel processing is a method that divides a task into multiple smaller sub-tasks, processed simultaneously. This approach is especially beneficial for AI computations, where vast amounts of data need to be processed in tandem. Hence, with its forward-thinking design, the H100 ensures it remains relevant, even as AI computations become increasingly intricate.


The MI250X from AMD stands as a testament to the brand’s commitment to versatility and adaptability. Every chip has a purpose, and the architecture of the MI250X is optimized for a spectrum of computational tasks. These tasks range from the complexities of deep learning algorithms to the demanding nature of real-time analytics.

However, it is not just about individual performance. In many computational scenarios, especially in data centers, chips don’t operate in isolation. They function as part of a more extensive networked system. Recognizing this, AMD has ensured that the MI250X possesses innate capabilities to work cohesively in clustered setups.

By clustering, multiple chips are combined to function as a singular, more powerful unit. Therefore, the AMD MI250X works well at enhancing the overall processing capacity. This means that as computational needs amplify, particularly in the realm of AI, the MI250X can be strategically paired with other units, accentuating its scalability and ensuring it remains adept at handling the future’s demands.

Software and Compatibility


NVIDIA has been at the forefront of AI and deep learning for quite some time. As a result, the H100 boasts extensive compatibility with a wide range of AI frameworks. Whether it’s TensorFlow, PyTorch, or Caffe, the H100 seamlessly integrates, allowing developers a hassle-free experience.

The software toolset that NVIDIA provides, especially CUDA, is a game-changer. CUDA, a parallel computing platform and programming model, unlocks the H100’s potential, enabling developers to squeeze out every ounce of performance from the chip. The extensive libraries, developer tools, and community support make it a go-to for many AI professionals.


H100 vs. MI250X
All AMD products have access to the company’s robust ecosystem of products.

While the MI250X might not have as broad compatibility as the H100, it is deeply integrated with AMD’s ecosystem. This deep integration means that when used within AMD’s environment, it’s optimized to offer peak performance. The open-source software platform ROCm (Radeon Open Compute) is AMD’s answer to NVIDIA’s CUDA.

Furthermore, it’s designed to provide GPU-accelerated computation and boasts a set of advanced tools and libraries tailored for the MI250X. When working on specialized tasks, especially within the AMD environment, these tools can provide an edge over generic solutions. So anyone working with AI datasets or deploying advanced algorithms already within the AMD product suite will highly favor this chip.

H100 vs. MI250X: 5 Must-Know Facts

  1. The AMD MI250X has a significant edge in raw performance benchmarks.
  2. The NVIDIA H100 is more power-efficient, offering enhanced performance with reduced power consumption at only 350W.
  3. While the H100 is competitive in memory, the MI250X excels in both data retrieval speeds and storage capacity.
  4. The MI250X provides exclusive access to AMD’s suite of proprietary tools.
  5. Considering the price, MI250X offers significant value, making it a go-to for budget-conscious professionals.

H100 vs. MI250X: Which One Is Better? Which One Should You Choose?

Choosing between NVIDIA and AMD is inherently difficult as both companies are quite literally world leaders in AI computing. Both GPUs are cutting-edge pieces of hardware, each designed to excel in their respective domains. However, as is the case with any technological decision, the answer boils down to the specifics of what you need, the ecosystem you operate within, and, of course, your budget.

Performance metrics are often the primary consideration for AI projects, especially when considering the H100 vs. MI250X. While the MI250X offers a superior (and more consistent) FP32 performance of 47.87 teraFLOPS for both FP32 and FP64, the H100 beats the MI250X with an FP32 performance of 51.22 teraFLOPS but lags slightly behind FP64 at 25.61 teraFLOPS. So, performance favors the MI250X, but it comes with the penalty of less efficiency.

Budget, however, often acts as the ultimate arbiter. The H100, with its price tag hovering around $30,000, is an investment for the dedicated. To make matters more difficult, the H100 chips are notoriously hard to find, with secondary market prices sometimes even pushing higher in price. In contrast, the MI250X, priced at roughly $10,000, offers a really great deal at a third of the cost.

For those unrestricted by budget and seeking a GPU with vast compatibility options, the H100 is a contender. If budget is a factor at all, it will be very difficult to choose against the MI250X. Ultimately, when choosing between the H100 vs. MI250X, the choice rests on individual priorities and use cases.

Frequently Asked Questions

Is NVIDIA H100 better than AMD MI250X for AI tasks?

It’s a close call. The H100 does pull ahead with an FP32 performance of 51.22 teraFLOPS, but it falls behind the MI250X in FP64 performance. Meanwhile, the MI250X maintains a consistent 47.87 teraFLOPS for both FP32 and FP64. Thus, for balanced performance, the MI250X might be the choice.

Which chip is more energy-efficient?

When it comes to energy efficiency, the NVIDIA H100 takes the lead. Operating at a TDP of 350W, it consumes less power compared to the AMD MI250X, which operates at a higher TDP of 500W.

How do these chips handle complex machine learning algorithms?

Both the H100 and MI250X are specifically engineered to manage the computational demands of intricate machine-learning tasks. Their architecture is vastly different from standard CPUs, as they process immense amounts of data simultaneously.

Which chip offers better memory capabilities?

The MI250X beats the H100 in both memory capacity and data retrieval rates. AMD’s AI chip features a robust memory of 128 GB compared to NVIDIA’s 80GB in the H100. Plus, the MI250X offers speeds of 3277GB/S, outpacing the H100 by over 1,000GB/s.

Are these chips compatible with all AI frameworks?

The NVIDIA H100 takes the cake for vast compatibility, making it versatile across multiple AI frameworks. In contrast, while the MI250X also boasts compatibility with many AI platforms, it particularly shines when integrated with AMD’s ecosystem of products.

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