AMD Instinct

AMD Instinct™ MI350 Series GPUs: Leadership Performance, Cost Efficient, Fully Open-Source

The AMD Instinct™ MI350 Series GPUs, launched in June 2025, represent a significant leap forward in data center computing, designed to accelerate generative AI and high-performance computing (HPC) workloads. Built on the cutting-edge 4th Gen AMD CDNA™ architecture and fabricated using TSMC's 3nm process, these GPUs deliver exceptional performance and energy efficiency for training massive AI models, high-speed inference, and complex HPC tasks like scientific simulations and data processing. Featuring 288GB of HBM3E memory and up to 8TB/s bandwidth, the MI350X and MI355X GPUs offer up to 4x generational AI compute improvement and a remarkable 35x boost in inference performance, positioning them as formidable competitors in the AI and HPC markets.

Optimize Next Gen Innovation with ROCm Software

The AMD ROCm™ 7.0 software stack is a key differentiator that enables high-performance AI and HPC development with minimal code changes. Instinct MI350 Series GPUs are fully optimized for leading frameworks such as PyTorch, TensorFlow, JAX, ONNX Runtime, Triton, and vLLM, and offer Day 0 support for popular models through automatic kernel generation and continuous validation. The ROCm 7.0 platform’ unique combination of DeepEP pipelining, SGL cross-PD scheduling, and PD KV-cache transfers deliver significant advantages. AMD is a founding member of the PyTorch Foundation and actively contributes to OpenXLA and UEC, reinforcing our long-term commitment to open-source AI. With AMD Infinity Hub, users gain access to deployment-ready containers that simplify onboarding and accelerate time to value. MI350 Series GPUs are purpose-built for scalable inference and training, with their elastic scaling capabilities and vendor-agnostic optimization enhanced by full Kubernetes integration enabled by the AMD GPU Operator.

AMD Instinct
MI355X GPU	MI350X GPU	Model	MI325X GPU
TSMC's N3P / TSMC's N6		Process Technology (XCD/IOD)	TSMC's N5 / TSMC's 6
AMD CDNA4		GPU Architecture	AMD CDNA3
256		GPU Compute Units	304
16,384		Stream Processors	19,456
185 Billion		Transistor Count	153 Billion
10.1 PFLOPs	9.2 PFLOPs	MXFP4/MXFP6	N/A
5.0 POPs	4.6 POPs	INT8	2.6 POPs
78.6 TFLOPS	72.1 TFLOPS	FP64(Vector)	81.7 TFLOPS
5.0 PFLOPS	4.6 PFLOPS	FP8	2.6 PFLOPS
2.5 PFLOPS	2.3 PFLOPS	BF16	1.3 PFLOPS
288 GB HBM3E		Dedicated Memory Size	256 GB HBM3E
Up to 8.0 TB/sec		Memory Bandwidth	6 TB/s
PCIe Gen5 x16		Bus Interface	PCIe Gen5 x16
Passive & Active	Passive	Cooling	Passive & Active
1400W	1000W	Maximum TDP/TBP	1000W
Up to 8 partitions		Virtualization Support	Up to 8 partitions

What's New in ROCm 7.0?

Expanded Hardware & Platform Support : ROCm 7 is fully compatible with AMD Instinct™ MI350 Series GPUs (including MXFP6/MXFP4) and extends development to select AMD Radeon™ GPUs and Windows environments, ensuring seamless performance across diverse hardware from cloud to edge.
Advanced AI Features & Optimizations : ROCm 7 is targeting large-scale AI and LLM deployments with pre-optimized transformer kernels (OCPFP8/MXFP8/MXFP6/MXFP4), integrated distributed inference via vLLM v1 and SGLang, and enhanced "flash" attention and communication libraries for peak multi-GPU utilization.
Optimized Performance* : The ROCm 7 preview delivered up to 3.5× faster AI inference and 3× quicker training than ROCm 6 by leveraging lower-precision data types and advanced kernel fusion to maximize GPU efficiency and reduce memory and I/O load.
Enabling Developer Success : With the new ROCm Enterprise AI suite, it is now easier to fine-tune models on domain-specific data and deploy AI services in production, streamline install with a pip install rocm flow, and support advanced optimization features such as model quantization libraries to boost productivity and performance.
Expanded Ecosystem & Community Collaboration : ROCm 7 deepens integration with leading AI and HPC models and frameworks, offering day-0 support for PyTorch, TensorFlow, JAX, and ONNX and more, while giving organizations flexibility in model selection with over 2 million pre-trained models. Its broad ecosystem and open-source collaboration ensures stability, compatibility, and readiness for future workloads.

*(MI300-080): Testing by AMD as of May 15, 2025, measuring the inference performance in tokens per second (TPS) of AMD ROCm 6.x software, vLLM 0.3.3 vs. AMD ROCm 7.0 preview version SW, vLLM 0.8.5 on a system with (8) AMD Instinct MI300X GPUs running Llama 3.1-70B (TP2), Qwen 72B (TP2), and Deepseek-R1 (FP16) models with batch sizes of 1-256 and sequence lengths of 128-204. Stated performance uplift is expressed as the average TPS over the (3) LLMs tested. Results may vary.

Select GIGABYTE for the AMD Instinct MI350 Series Platform

Compute Density

Offering industry leading compute density servers in a 8U air cooled G893 series and a 4U liquid-cooled G4L3 series, servers achieve greater performance/rack.

High Performance

The custom 8-GPU UBB-based server ensures stable and peak performance from CPUs & GPUs as priority was given to signal integrity and cooling.

Scale-out

Multiple expansion slots are available to be populated with ethernet or InfiniBand NICs for high-speed communications between interconnected nodes.

Advanced Cooling

With the availability of server models using direct liquid cooling (DLC), CPUs and GPUs can have heat removed faster and more efficiently with liquid cooling than air.

Energy Efficiency

Real-time power management, automatic fan speed control, redundant Titanium PSUs, and DLC option ensure the best cooling and power efficiency. Also, DLC option.

AMD Instinct™ MI300 Series GPUs

OverviewGPU & APUSpecifications

Accelerators for the Exascale Era

Frontier is the #1 fastest supercomputer in the TOP500 and one of the greenest in the Green500 with AMD EPYC™ processors and AMD Instinct™ GPUs. These technologies are now avaialable in GIGABYTE servers for high performance computing (HPC), AI training & inference, and data intensive workloads.
With AMD's data center APU and discrete GPUs, GIGABYTE has created and tailored powerful, passive and liquid-cooled servers to deliver accelerators for the Exascale era. The AMD Instinct™ MI325X and MI300X GPUs are designed for AI training, fine tuning and inference. They are Open Accelerator Modules (OAMs) on a universal baseboard (UBB) housed inside GIGABYTE G-series servers. The AMD Instinct MI300A integrated CPU/GPU accelerated processing unit (APU) targets HPC and AI. It comes in an LGA socketed design with four sockets in GIGABYTE G383 series servers.
El Capitan is projected to be the world's most powerful supercomputer capable of performing more than 2 exaflops per second. At the heart of the new machine is the AMD Instinct MI300A APU, designed to overcome prformance bottlenecks from the narrow interfaces between CPU and GPU, programming overhead for managing data, and the need to modify code for GPU generations. The MI300A APU architecture has a chiplet design where the AMD Zen4 CPUs and AMD CDNA™3 GPUs share unified memory. This means that the technology is not only used to support small deployments such as a single server, but it is also able to scale for large computing clusters. The demand for AI and HPC is here, and GIGABYTE has the technologies you need to win.

A Discrete GPU and a Data Center APU

The AMD Instinct MI300 Series, including MI300X & MI325X, and MI300A, are designed to boost AI and high-performance computing (HPC) capabilities in a compact, efficient package that reduces total cost of ownership.

Designed to power the largest AI models, the AMD Instinct MI325X GPU feature industry-leading 256GB of memory and 6 TB/s bandwidth to speed response times. Enhanced power efficiency and support for matrix sparsity further optimize AI training and inference, helping enable sustainable scaling of AI solutions across data centers.

The Instinct MI300X offers raw acceleration power with eight GPUs per node on a standard platform. The AMD Instinct MI300 Series aims to improve data center efficiencies, tackle budget and sustainability concerns, and provide a highly programmable GPU software platform. It features advanced GPUs for generative AI and HPC, high-throughput AMD CDNA 3 GPU CUs, and native hardware sparse matrix support. The MI300X delivers 304 high-throughput compute units for AI-specific functions including new data-type support, photo and video decoding. It supports a wide range of data types and the latest Generative AI models.

The world's first unified CPU/GPU accelerated processing unit (APU) - Instinct MI300A - is built to overcome performance bottlenecks from the narrow interfaces between CPU and GPU, eliminate the programming overhead for managing data, and the need to refactor and recompile code for every GPU generation. Deployed at scale in some of the world's fastest and greenest supercomputers, this technology is now available to right-sized for your environment through GIGABYTE.

By enhancing computational throughput and simplifying programming and deployment, the AMD Instinct MI300 Series addresses the escalating demand for AI and accelerated HPC amidst resource, complexity, speed, and architecture challenges. The AMD Instinct MI300 Series is Ready to Deploy.

AMD Instinct™ MI300 Series Accelerators Specifications

MI325X GPU	MI300X GPU	Model	MI300A APU
OAM module		Form Factor	APU SH5 socket
-		AMD ‘Zen 4’ CPU cores	24
304		GPU Compute Units	228
19,456		Stream Processors	14,592
163.4 TFLOPS		Peak FP64/FP32 Matrix*	122.6 TFLOPS
81.7/163.4 TFLOPS		Peak FP64/FP32 Vector*	61.3/122.6 TFLOPS
1307.4 TFLOPS		Peak FP16/BF16*	980.6 TFLOPS
2614.9 TFLOPS		Peak FP8*	1961.2 TFLOPS
256 GB HBM3E	192 GB HBM3	Dedicated Memory Size	128 GB HBM3
6.0 GHz	5.2 GHz	Memory Clock	5.2 GHz
6 TB/s	5.3 TB/s	Memory Bandwidth	5.3 TB/s
PCIe Gen5 x16		Bus Interface	PCIe Gen5 x16
8		Infinity Fabric™ Links	8
1000W	750W	Maximum TDP/TBP	550W / 760W (Peak)
Up to 8 partitions		Virtualization Support	Up to 3 partitions

*Indicates not with sparsity

Applications for AMD Instinct Series

AI Inference

High memory bandwidth, large memory capacity, and low latency between GPUs are ideal for AI inference for their ability to handle large data sets and processing data in batches. This is important for real-time or large-scale inference applications.

Generative AI

8-GPU UBB based servers are ideal for generative AI because of the parallel processing nature of the GPU. Parallel processing is great for massive training data sets and running deep learning models like neural networks, and it speeds up applications like natural language processing and data augmentation.

HPC

Complex problem solving in HPC applications involves simulations, modeling, and data analysis to achieve greater insights. Parallel processing from the GPU is needed, but also there is heavy reliance on the CPU for sequential processing in mathematical computations.