AMD MI300X Accelerator Unpacked: Specs, Performance, & More

Generative AI and HPC are pushing hardware to its limits—and AMD’s Instinct™ MI300X GPU Accelerator is stepping up to the challenge. AMD packed the MI300X with a whopping 304 GPU compute units and 192 GB of HBM3 memory with 5.3 TB/s bandwidth.

For startups and enterprises alike, these specs translate directly to faster development cycles and more cost-effective AI and HPC deployments. Even more telling, major cloud providers like Microsoft Azure and tech giants like Meta have begun integrating the MI300X into their AI infrastructure.

But what exactly does the MI300X mean for your specific workload? Can it match or outperform direct competitors like NVIDIA’s H100 in real-world applications? And does its price-performance ratio justify the investment?

To answer these questions, we've broken down everything you need to know about the MI300X GPU accelerator below, from its robust architecture to its performance benchmarks.

AMD Instinct MI300X: The Big Picture

The AMD Instinct MI300X is a high-performance GPU accelerator designed to tackle the increasing demands of generative AI and high-performance computing (HPC) workloads.

Officially released on December 6, 2023, the MI300X represents AMD’s most determined effort to challenge NVIDIA’s near-monopoly in the AI GPU market, especially for memory-intensive applications like large language models (LLMs).

This exceptional GPU accelerator arrived at a critical moment in AI development. As models grew to billions and trillions of parameters, memory capacity and bandwidth became just as important as raw computing power.

AMD spotted this market gap and positioned the MI300X as a memory-focused alternative to NVIDIA’s offerings. At the time of this writing, the MI300X is AMD’s second most powerful AI accelerator in their commercial lineup, sitting just below the flagship MI325X.

For AI startups facing tight budgets and enterprises looking to scale, the MI300X provides a compelling alternative that doesn’t require the workarounds often needed with relatively memory-limited GPUs like NVIDIA’s H100.

The MI300X also marks AMD’s return to competitive positioning in the AI and HPC market after years of trailing behind NVIDIA. It’s part of a broader strategy that includes the MI300A (AMD’s CPU-GPU hybrid for supercomputing) and the ROCm software platform—all forming a comprehensive game plan to reclaim market share in the AI space.

Inside the AMD MI300X: Core Specifications & Architecture

The MI300X’s technical foundation explains why it’s gained traction so quickly among AI teams, developers, and researchers who need both power and memory. Let’s take a closer look.

Hardware Specifications

The MI300X is easily one of the most powerful AI GPU accelerators available today. Let’s take a closer look under the hood:

Specifications	Details
Architecture	AMD CDNA™ 3
Supported Technologies	AMD CDNA™ 3 Architecture, AMD ROCm™ - Ecosystem without Borders, AMD Infinity Architecture
Compute Units	304 GPU Compute Units
Matrix Cores	1,216
Stream Processors	19,456
Memory Size & Type	192 GB HBM3
Peak Memory Bandwidth	5.3TB/s
Processing Power	2.6 PFLOPS (FP8), 1.3 PFLOPS (FP16) per GPU
Scalability	Up to 20.8 PFLOPS (FP8) and 10.4 PFLOPS (FP16) in an eight-GPU setup
Last Level Cache (LLC)	256MB
Peak Engine Clock	2,100 MHz
Transistor Count	153 Billion
Process Technology	TSMC 5nm & 6nm
Power Draw (TBP)	750W

Chiplet-Based Design: A Break from Tradition

Instead of a single monolithic GPU die, the MI300X stacks multiple chiplets together. Specifically, it uses:

• Eight accelerator complex dies (XCDs) containing the actual compute cores
• Four I/O dies (IODs) to handle memory management, connectivity, and data routing
• All connected via AMD’s Infinity Fabric technology

This modular approach improves manufacturing yields and allows AMD to create different configurations from the same basic components (like the MI300A, which combines GPU with EPYC CPU cores).

CDNA 3 Architecture: What’s New?

The MI300X is built on AMD’s CDNA 3 architecture, an evolution from CDNA 2. The biggest change? A chiplet-based design that replaces a traditional monolithic GPU die, which allows for better scalability, power efficiency, and performance.

In practice, AMD’s CDNA 3 offers the following improvements:

• Higher Compute Density: The MI300X crams more compute units and transistors into a smaller, more power-efficient package.
• Enhanced AI Performance: Optimizations in matrix operations and mixed-precision computing improve deep learning performance.
• Lower Latency Data Movement: Infinity Fabric™ technology provides faster, low-latency communication between chiplets and memory.

Memory and Bandwidth

Memory capacity and bandwidth are arguably the MI300X’s most notable advantages. It boasts 192 GB of HBM3 memory, which is well over double NVIDIA’s H100 SXM at 80 GB. This means larger AI models can fit entirely in the MI300X’s memory for faster data transfers.

With 5.3 TB/s of memory bandwidth, the MI300X can also move data faster than nearly any GPU on the market. For context, NVIDIA’s H100 has 3.35 TB/s bandwidth.

At a glance, the MI300X’s memory advantages mean:

• Faster model training and inference due to quicker access to parameters.
• Reduced bottlenecks in deep learning workflows by keeping data closer to compute units.

Power Efficiency: Balancing Performance and Consumption

Despite its high performance, the MI300X maintains competitive power efficiency. At 750W Typical Board Power (TBP), it’s comparable to NVIDIA’s H100 (700W) while delivering higher memory capacity and bandwidth per watt.

This relatively lower energy consumption is thanks to the MI300X’s chiplet design and CDNA 3 optimizations, which allow it to handle intensive workloads without massive power draw.

AMD MI300X vs NVIDIA H100: The Performance Battle

Specifications look impressive on paper, but the real-world application is where they truly matter. AMD’s MI300X and NVIDIA's H100 are two of the most powerful accelerators available today. But how do they perform in actual workloads? Let’s find out:

LLM Inference Performance

As you’d expect, the MI300X’s memory advantage translates directly to superior performance on large language models (LLMs).

Various real-world testing (including our benchmarks at TensorWave) confirms these theories and shows the MI300X’s strength in large language model inference:

• Mixtral 8x7B: The MI300X achieves 33% higher throughput compared to the H100 SXM when running this popular Mixture of Experts (MoE) model using MK1's inference software.
• Chat applications: In real-world scenarios requiring fast response times, the MI300X consistently outperforms the H100 in both offline and online inference tasks.
• Context handling: The MI300X’s larger memory capacity also allows it to handle longer contexts without performance degradation.

The MI300X performs exceptionally well with MoE architectures—the same architectures powering top models from Mistral, Meta, Databricks, and X.ai.

AI Training Workloads

AI model training thrives on raw compute power and high memory bandwidth. The H100 SXM features 132 streaming multiprocessors (SMs) and 80 GB of HBM3 memory, while the MI300X boasts 304 compute units and 192 GB of HBM3.

Looking at various benchmarks:

• The MI300X’s larger memory pool allows it to train massive AI models without splitting them across multiple GPUs, reducing data movement overhead.
• H100’s Tensor Cores are optimized for deep learning, giving it an edge in mixed-precision training (FP8 and FP16).
• In real-world benchmarks, the MI300X trains large language models (LLMs) more efficiently because it avoids memory bottlenecks, while the H100 excels in smaller, high-speed training tasks.
• Studies have found that using MI300X for LLAMA 2-70B inference results in a 40% latency advantage compared to H100 deployments, thanks to its superior memory bandwidth.

In pure AI performance testing, AMD found that the MI300X delivers up to 1.3X the performance of NVIDIA H100:

HPC & Scientific Computing

Beyond AI, both the MI300X and H100 GPUs power high-performance computing (HPC) applications in weather simulations, molecular dynamics, and physics modeling.

At this point, things become a bit more nuanced. The H100’s CUDA ecosystem is deeply integrated into HPC workflows, making it a natural fit for traditional scientific computing.

That said, the MI300X benefits from its higher memory bandwidth, which allows for faster data access in memory-heavy simulations. According to AMD, the MI300X pulls ahead of the H100 by up to 2.4X in HPC performance:

Power Efficiency & Total Cost of Ownership (TCO)

Power consumption directly impacts operating costs. The MI300X and H100 have almost identical peak power draws (750W and 700W, respectively). But, of course, their efficiency varies based on workload:

• The MI300X consolidates workloads, which reduces the need for multi-GPU setups. And the fewer the GPUs, the lower the power draw per task.
• The H100 handles smaller models efficiently, but scaling to larger models increases power usage due to data movement overhead.
• AMD’s chiplet-based design improves thermal efficiency for better performance-per-watt in memory-heavy workloads.

Our Verdict?

Both AMD’s MI300X and NVIDIA's H100 are powerful in their own right, but the right GPU for you depends on your specific AI workload and needs.

Category	AMD MI300X	NVIDIA H100
Memory Capacity	192 GB HBM3 (fits larger AI models natively)	80 GB HBM3 (may require model splitting)
Compute Power	304 GPU compute units	132 SMs with Tensor Cores
AI Training	Faster for large models	Faster for small-to-mid models
AI Inference	Better for massive LLMs	Better for high-throughput inference
HPC Workloads	Excels in memory-heavy tasks	Better CUDA support
Power Efficiency	Lower power per large model	Lower power per small model
Ecosystem	Strong for AI and memory-bound workloads	Mature CUDA software stack

The MI300X is the better choice for massive AI models, high-memory tasks, and reducing multi-GPU overhead, while the H100 excels in CUDA-heavy environments and optimized deep learning workloads.

AMD MI300X: Adoption and Industry Shifts

The MI300X has gained notable traction since its December 2023 launch. While it still has some ways to go adoption-wise, its growing popularity promises to break NVIDIA’s stranglehold on the AI GPU accelerator market.

Microsoft Azure was the first major cloud provider to adopt the MI300X, integrating it into their ND MI300X v5 Virtual Machines. They’ve since expanded availability across multiple regions, citing strong customer demand, particularly for memory-intensive LLM workloads.

Oracle Cloud followed shortly after, offering MI300X bare metal instances in their OCI AI infrastructure. Not long after, IBM Cloud joined the fray in late 2024 and is set to officially add MI300X options to its AI portfolio in the first half of 2025.

Cloud providers aside, tech giants like Meta, Dell, HPE, Lenovo, and others have all added the MI300X to their internal AI research infrastructure, while supercomputing leaders like the Department of Energy’s El Capitan use the related MI300A.

This growing adoption is slowly but surely creating a more competitive market. AI teams and developers now have more affordable and scalable alternatives to NVIDIA’s offerings that simply didn’t exist before the MI300X launch.

AMD MI300X: Challenges and Limitations

The MI300X is a strong competitor in the AI accelerator market, but it’s not without its drawbacks. While AMD has, no doubt, made significant progress in the industry, some areas still lag behind NVIDIA’s ecosystem:

• Software and Ecosystem Maturity: One of the biggest hurdles AMD has to cross is software. NVIDIA’s CUDA and TensorRT have been industry standards for years, with deep support across AI frameworks. While AMD’s Radeon Open Compute (ROCm) is steadily improving, it currently lacks the same level of developer adoption, which means some AI models may need additional optimization to run as efficiently.
• Compatibility and Developer Learning Curve: Many AI teams have built their workflows around NVIDIA hardware. Switching to AMD’s ecosystem requires adapting models and restructuring approaches. While libraries like PyTorch and TensorFlow are adding better support for AMD GPUs, some applications may require manual tuning.

It goes without saying that AMD is actively working to overcome these barriers. And despite these challenges, the MI300X offers unique advantages in memory capacity, scalability, and cost-efficiency that are sure to give NVIDIA a run for its money.

Power Your AI Workloads with TensorWave’s AMD MI300X Cloud

Looking to put the MI300X’s power to work without the hardware investment? TensorWave offers immediate access to these accelerators through its specialized AI cloud platform.

TensorWave is powered by the MI300X, with optimized containers and frameworks that extract maximum performance from AMD’s architecture. Our inference engine takes full advantage of the MI300X’s high memory capacity and bandwidth, letting you run large AI models without costly sharding or parameter offloading.

You get a user-friendly, scalable infrastructure that lets you test the MI300X before committing, grow as your needs evolve, and achieve faster results at a lower total cost of ownership.

For AI startups and enterprises alike, TensorWave provides a direct, seamless path to AMD's MI300X GPU accelerators without the upfront costs. Get in touch today.

Key Takeaways

AMD’s MI300X accelerator marks a significant shift in the AI hardware space. Many consider it the release that finally gives NVIDIA meaningful competition after AMD’s years of “second place” in the AI GPU market.

Short on time? Here’s a rundown of the highlights:

• With 19 2GB of HBM3 memory and 5.3 TB/s bandwidth, the MI300X eliminates many workarounds needed for large AI models. This translates to simpler deployments and faster development cycles for memory-intensive applications.
• Major cloud providers like Azure and Oracle now offer MI300X instances to make this stellar accelerator easily accessible without a massive upfront investment. Specialized players like TensorWave take things further with a MI300X-optimized platform.
• While the H100 still holds its own in the AI GPU market, the MI300X shines for large language models and applications where memory constraints are the primary bottleneck.

Ready to innovate? TensorWave and the MI300X are here to power your next breakthrough. Experience the difference today.