AMD Instinct

AMD Instinct is a family of high-performance GPU accelerators developed by Advanced Micro Devices (AMD) for data center applications, specifically optimized for artificial intelligence (AI), high-performance computing (HPC), and machine learning workloads.^[1] These accelerators leverage specialized architectures to deliver exceptional compute density, memory bandwidth, and efficiency, enabling scalable solutions for generative AI training and inference, scientific simulations, and large-scale data processing.^[2] The Instinct lineup is built on AMD's CDNA (Compute DNA) architecture, distinct from the consumer-oriented RDNA series, with successive generations like CDNA 3 (used in MI300 Series) and the fourth-generation CDNA (in MI350 Series, launched in 2025) incorporating advanced Matrix Core Technologies for accelerated tensor operations.^[1] Key features include support for a wide range of data precisions—such as FP64 for HPC precision, FP16/BF16 with sparsity for AI efficiency, and emerging formats like MXFP4 and MXFP6—to optimize performance and energy use across diverse workloads.^[2] High-bandwidth memory (HBM) variants, such as 192 GB HBM3 in the MI300X or 256 GB HBM3E in the MI325X, provide up to 6 TB/s bandwidth, facilitating massive parallelism in systems scaling to thousands of GPUs.^[2] Notable products in the Instinct portfolio include the MI200 Series for early exascale computing, the MI300 Series (encompassing the GPU-only MI300X, the APU-integrated MI300A with Zen 4 CPU cores for unified memory access, and the MI325X for enhanced AI inference), and the MI350 Series, which advances efficiency with next-generation datatypes.^[1] Supported by the open-source ROCm software stack, which offers programming models, libraries, and tools for developer accessibility, Instinct accelerators power leading supercomputers like El Capitan^[3] and are deployed in cloud environments for enterprise AI.^[2] As of 2025, the platform has seen significant adoption, contributing to AMD's data center revenue growth through its focus on open ecosystems and hardware-software integration.^[4]

History

Origins and launch

The origins of the AMD Instinct series trace back to AMD's FirePro S series of data center GPUs introduced in early 2016, which marked the company's initial foray into specialized server accelerators for high-performance computing (HPC) workloads.^[5] The FirePro S7150, launched on February 1, 2016, was a Tonga-based GPU designed for professional visualization and compute tasks in data centers, featuring 8 GB of GDDR5 memory and support for multi-user virtualization via MxGPU technology.^[6] Similarly, the FirePro S9300 x2, a dual-GPU card based on the Fiji architecture with 8 GB of HBM memory, targeted HPC applications such as scientific simulations and data analytics, offering up to 13.9 TFLOPS of single-precision performance.^[7] These products laid the groundwork for AMD's data center GPU strategy by emphasizing scalability, energy efficiency, and integration with open-source software stacks like ROCm, which began supporting deep learning primitives in 2016.^[8] AMD officially unveiled the Radeon Instinct brand on December 12, 2016, positioning it as a dedicated line of accelerators for machine intelligence and deep learning to rival NVIDIA's Tesla products in the burgeoning AI market.^[9] The brand launch emphasized an open ecosystem approach, including hardware optimized for inference and training alongside software libraries to accelerate adoption.^[10] This initiative built directly on the FirePro S series by shifting focus toward AI-specific optimizations while retaining HPC compatibility. The Instinct lineup was announced with plans for availability in the first half of 2017, aligning with AMD's broader push into data center computing amid growing demand for GPU-accelerated AI.^[11] The first Instinct products, the MI6 and MI8 accelerators, were released in June 2017 as entry-level options for deep learning deployments.^[12] The MI6, based on the Polaris 10 architecture, delivered 5.73 TFLOPS of single-precision (FP32) performance at a thermal design power (TDP) of 150 W, with 16 GB of GDDR5 memory suited for inference tasks in power-constrained environments.^[12] The MI8, utilizing the Fiji GPU architecture, provided 8.9 TFLOPS FP32 performance at 175 W TDP and 4 GB of HBM memory, enabling efficient handling of data-parallel workloads.^[12] Both accelerators prioritized deep learning acceleration, offering native support for popular frameworks such as Caffe and TensorFlow through AMD's MIOpen library and the emerging ROCm platform.^[13] Early adoption of Instinct accelerators was bolstered by integrations with major cloud providers, including initial support in Microsoft Azure environments for AI workloads starting in 2017.^[14] This partnership facilitated broader accessibility for developers, allowing seamless deployment of Instinct hardware in virtualized data centers for training and inference applications. The transition to the ROCm software stack further enhanced compatibility, enabling porting of TensorFlow by early 2017.^[13]

Evolution and rebranding

The AMD Instinct lineup advanced significantly in 2018 with the release of the Radeon Instinct MI50 and MI60 accelerators, built on the 7nm Vega 20 architecture. These GPUs marked AMD's entry into high-memory, high-performance computing (HPC) workloads optimized for deep learning and scientific simulations, featuring up to 32 GB of HBM2 memory and PCIe 4.0 support for enhanced bandwidth. The MI50, targeted at inference tasks, offered configurations with 16 GB or 32 GB HBM2 and a peak FP32 performance of 13.3 TFLOPS at a 300W TDP, while the MI60, designed for training, provided 32 GB HBM2 and up to 14.7 TFLOPS FP32 in a similar power envelope. This shift to 7nm process technology improved energy efficiency and compute density compared to prior Vega-based models like the MI25, enabling better scalability in datacenter environments.^[15]^[12] In 2019, AMD expanded the software ecosystem supporting the Instinct series, with the release of ROCm 3.0 at Supercomputing 2019, which broadened compatibility across Linux distributions such as Ubuntu 18.04 and CentOS 7.6, alongside integration with updated compilers and machine learning frameworks like PyTorch and TensorFlow. This update enhanced developer accessibility for HPC and AI applications, fostering adoption in diverse workloads. Despite these advancements, the Instinct lineup faced stiff market challenges from Nvidia's Volta (V100) and emerging Ampere (A100) architectures, which dominated GPU-accelerated supercomputing due to mature software stacks and ecosystem lock-in. Early deployments of Instinct accelerators appeared in select HPC clusters, demonstrating viability in mixed-precision computing for research institutions.^[16]^[17] Ahead of the MI100 launch in November 2020, AMD rebranded the series from "Radeon Instinct" to simply "AMD Instinct" to underscore its compute-centric focus, distancing from graphics heritage and aligning with specialized datacenter acceleration. This rebranding coincided with the transition to the CDNA architecture in MI100, positioning Instinct as a dedicated HPC and AI platform amid intensifying competition.^[18]^[19]

Products

Early accelerators (MI6, MI8, MI25)

The AMD Instinct MI6 accelerator, based on the Polaris architecture, features 36 compute units with 2,304 stream processors, 16 GB of GDDR5 memory, and delivers 5.73 TFLOPS of single-precision (FP32) performance alongside 358 GFLOPS of double-precision (FP64) performance.^[12] Designed for entry-level deep learning inference tasks, it operates at a 150 W TDP and emphasizes efficiency with up to 38 GFLOPS per watt in FP16 or FP32 operations.^[9] The MI8, utilizing the Graphics Core Next (GCN) 3rd generation architecture, includes 64 compute units with 4,096 stream processors and 4 GB of HBM memory, achieving 8.9 TFLOPS FP32 and 512 GFLOPS FP64 performance at a 175 W TDP.^[12] Optimized for multi-precision computing in small-scale high-performance computing (HPC) environments, it supports workloads requiring high memory bandwidth through its 512 GB/s interface.^[12] The MI25 accelerator, built on the Vega architecture, incorporates 64 compute units with 4,096 stream processors and 16 GB of HBM2 memory (with a variant offering 32 GB), providing 12.29 TFLOPS FP32 and 768 GFLOPS FP64 (at 1/16 rate) performance while consuming 300 W.^[12] It includes Infinity Fabric interconnects for enhanced multi-GPU scaling in distributed systems.^[12] All three early Instinct accelerators share common features such as a PCIe 3.0 x16 interface for host connectivity, passive cooling designs suited for data center airflow, and support for SR-IOV virtualization to enable efficient resource partitioning in virtualized environments.^[12] Early applications of these accelerators included drug discovery simulations, where the MI25 facilitated virtual screening of molecular compounds to accelerate candidate identification, and financial modeling tasks involving risk assessment and predictive analytics.^[20] Benchmarks demonstrated that the MI25 achieved up to 2x the performance of previous-generation AMD FirePro accelerators in deep learning frameworks like Caffe for image classification workloads.^[21] These products laid foundational hardware advancements that influenced subsequent CDNA-based designs in the Instinct lineup.^[9]

Accelerator	Architecture	Compute Units / Stream Processors	Memory	FP32 Performance	FP64 Performance	TDP	Key Optimization
MI6	Polaris	36 / 2,304	16 GB GDDR5	5.73 TFLOPS	358 GFLOPS	150 W	Entry-level DL inference
MI8	GCN 3rd Gen	64 / 4,096	4 GB HBM	8.9 TFLOPS	512 GFLOPS	175 W	Multi-precision small-scale HPC
MI25	Vega	64 / 4,096	16 GB HBM2 (32 GB variant)	12.29 TFLOPS	768 GFLOPS	300 W	Multi-GPU scaling for simulations

MI100 and MI200 series

The AMD Instinct MI100 accelerator, codenamed Arcturus and launched in November 2020, marked the introduction of AMD's CDNA 1 architecture, a purpose-built compute design optimized for high-performance computing (HPC) and artificial intelligence (AI) workloads.^[22] It features 120 compute units, 7,680 stream processors, and 32 GB of HBM2 memory with a 1.2 TB/s bandwidth, delivering 11.5 TFLOPS of peak FP64 performance and 184.6 TFLOPS of FP16 performance at a 300 W TDP.^[22] This architecture includes dedicated matrix cores to accelerate tensor operations for AI, while supporting full-rate FP64 computations essential for scientific simulations, representing a shift from graphics-derived designs like the prior Vega-based accelerators.^[18] The MI200 series, based on the CDNA 2 architecture and released in 2021 under the codename Aldebaran, extends this compute focus with enhanced scalability and efficiency, targeting exascale HPC and large-scale AI training.^[23] The lineup includes the MI210 with 104 compute units, 64 GB HBM2e memory, 1.6 TB/s bandwidth, 22.6 TFLOPS FP64 vector performance, and 300 W TDP; the MI250 with 208 compute units, 128 GB HBM2e, 3.2 TB/s bandwidth, 45.3 TFLOPS FP64 vector performance, and up to 560 W TDP; and the flagship MI250X with 220 compute units, 128 GB HBM2e, 3.2 TB/s bandwidth, 47.9 TFLOPS FP64 vector and 95.7 TFLOPS FP64 matrix performance, 383 TFLOPS BF16 performance, and up to 560 W TDP.^[24]^[25] These accelerators build on HBM2e memory technology evolved from Vega-era implementations for high-bandwidth data movement in compute-intensive tasks.^[23] Key innovations in the CDNA 1 and 2 architectures include specialized matrix cores for dense linear algebra operations in AI and HPC, enabling efficient handling of mixed-precision workloads from FP64 to BF16.^[26] The MI200 series advances interconnectivity with third-generation Infinity Fabric technology, supporting up to eight links for 400 GB/s aggregate bandwidth between accelerators and AMD EPYC CPUs, facilitating coherent multi-GPU scaling in large clusters.^[27] Full-rate FP64 support persists across both generations, ensuring precision for simulations without the rate reductions common in graphics GPUs.^[26] A major deployment milestone for the MI200 series is the MI250X's role in the Frontier supercomputer at Oak Ridge National Laboratory, which achieved exascale performance in 2022 and claimed the top position on the TOP500 list with 1.102 exaFLOPS on the High-Performance Linpack (HPL) benchmark.^[28] In HPC benchmarks like HPL, the MI250X delivers approximately 2.5 times the FP64 performance uplift over the MI100, underscoring its impact on scalable scientific computing.^[29]

Model	Compute Units	Memory	Bandwidth	FP64 Vector (TFLOPS)	FP64 Matrix (TFLOPS)	BF16 (TFLOPS)	TDP (W)
MI100	120	32 GB HBM2	1.2 TB/s	11.5	N/A	N/A	300
MI210	104	64 GB HBM2e	1.6 TB/s	22.6	45.3	181	300
MI250	208	128 GB HBM2e	3.2 TB/s	45.3	90.5	362	560
MI250X	220	128 GB HBM2e	3.2 TB/s	47.9	95.7	383	560

MI300 series and MI325X

The AMD Instinct MI300 series, based on the CDNA 3 architecture, represents a significant advancement in integrated compute for high-performance computing (HPC) and artificial intelligence (AI) workloads, introducing unified memory models and enhanced matrix acceleration. Launched in 2023, the series includes the MI300A accelerated processing unit (APU) and the MI300X discrete GPU, both leveraging a chiplet-based design for scalability and efficiency. The CDNA 3 architecture features second-generation matrix cores that deliver up to 6.8 times the integer performance of the prior generation through optimized sparse operations, alongside native support for low-precision formats such as FP8 and INT8 to accelerate AI training and inference. This design enables seamless data sharing between compute elements, reducing latency in large-scale simulations and model deployments. The MI300A APU integrates 24 Zen 4 CPU cores with 228 CDNA 3 GPU compute units (CUs), providing 128 GB of unified HBM3 memory accessible by both CPU and GPU for streamlined HPC applications. It achieves 61.3 TFLOPS of FP64 vector performance and up to 2.61 PFLOPS in FP8 operations, with a thermal design power (TDP) of 760 W under liquid cooling, making it ideal for environments requiring cohesive CPU-GPU orchestration, such as scientific simulations. In contrast, the MI300X discrete GPU employs 304 CDNA 3 CUs and 192 GB of HBM3 memory, delivering 81.7 TFLOPS of FP64 vector performance, 2.61 PFLOPS of INT8 throughput, and 5.3 TB/s of memory bandwidth at a 750 W TDP. This configuration excels in memory-intensive AI tasks, supporting models up to 80 billion parameters on a single device without partitioning. Building on the MI300 foundation, the MI325X, introduced in Q4 2024, refines the CDNA 3 architecture with upgraded memory subsystems for demanding AI training scenarios. It features 256 GB of HBM3E memory and 6 TB/s bandwidth, enabling 1.3 times the compute performance of the MI300X while maintaining compatibility with existing platforms. Operating at a 1000 W TDP, the MI325X prioritizes efficiency in foundation model fine-tuning and inference, with peak FP64 performance reaching 81.7 TFLOPS in vector operations and up to 163.4 TFLOPS in matrix modes. These enhancements position the MI325X as a bridge to next-generation designs, such as those incorporating CDNA 4 in the MI350 series. Key achievements of the MI300 series include powering the El Capitan supercomputer at Lawrence Livermore National Laboratory, which leverages the MI300A to deliver over 1.7 exaFLOPS and secured the top position on the TOP500 list in November 2024, maintaining leadership into 2025. In AI applications, the MI300X demonstrates substantial inference improvements, achieving up to 3.5 times faster token generation on large language models compared to baseline configurations, highlighting its impact on generative AI efficiency.

MI350 series

The AMD Instinct MI350 series, built on the 4th generation CDNA architecture and fabricated on a 3 nm process node, represents AMD's 2025 advancements in AI and high-performance computing (HPC) accelerators, emphasizing energy efficiency and scalability for large-scale data center deployments.^[30]^[12] This series builds briefly on the chiplet-based design of prior generations, incorporating multiple accelerator complex dies (XCDs) for enhanced modularity.^[31] The lineup includes the MI350X and the enhanced MI355X variants, both featuring 288 GB of HBM3E memory per GPU and 8 TB/s memory bandwidth to handle memory-intensive AI models.^[32] The MI350X, launched in 2025 with a 1000 W TDP, delivers 72 TFLOPS of FP64 performance and up to 9.2 PFLOPS in FP8 tensor operations (with structured sparsity), enabling efficient HPC workloads while maintaining air-cooling compatibility for standard data center infrastructures.^[30]^[33] The MI355X, an upgraded model with a 1400 W TDP, boosts capabilities to 5 PFLOPS in FP16 tensor operations (with structured sparsity) and 10.1 PFLOPS in FP8 (with structured sparsity), supporting advanced precisions like FP6 and FP4 for next-generation inference tasks; it became generally available in Q3 2025 through partners including Vultr.^[34]^[33]^[35] Key innovations in the CDNA 4 architecture include third-generation matrix cores that double throughput for low-precision formats like FP8 and BF16 compared to prior generations, alongside advanced sparsity acceleration to optimize sparse matrix operations in AI training and inference.^[26]^[36] Integration with UALink enables seamless multi-rack scaling for hyperscale environments, facilitating interconnects across thousands of GPUs without proprietary constraints.^[37] Performance benchmarks highlight up to 4x generation-on-generation improvement in AI compute over the MI300 series, surpassing AMD's 2020 five-year goal for energy efficiency in AI training and HPC by delivering up to 35x gains in inference performance relative to the MI250X.^[38]^[39] In MLPerf evaluations, the MI350 series demonstrated leadership in both training and inference for large language models like Llama 70B, with up to 1.3x advantages in select inference workloads against competitors.^[40]^[41] Adoption has accelerated in hyperscale AI data centers, with the series powering AMD's Helios rack-scale systems—announced in October 2025 as an open-standard platform compliant with OCP and UALink for up to 50% more memory density than rival designs—targeting deployments starting in 2026 through partners like Oracle.^[42]^[43] Early integrations via cloud providers like Vultr underscore its role in enabling cost-optimized AI agent and content generation workloads.^[35]

Model	Memory	Bandwidth	TDP	Key Peak Performance
MI350X	288 GB HBM3E	8 TB/s	1000 W	FP64: 72 TFLOPS FP8 Tensor: 9.2 PFLOPS (with structured sparsity)^[33]
MI355X	288 GB HBM3E	8 TB/s	1400 W	FP16 Tensor: 5 PFLOPS (with structured sparsity) FP8 Tensor: 10.1 PFLOPS (with structured sparsity)^[33]

Software ecosystem

ROCm platform

The ROCm (Radeon Open Compute) platform is an open-source software stack developed by AMD for GPU-accelerated computing, initially released on November 14, 2016.^[44] It has evolved significantly, reaching version 7.0.2 by October 2025 and version 7.1 on October 30, 2025, with ongoing updates enhancing performance, compatibility, and developer tools for high-performance computing (HPC) and artificial intelligence (AI) workloads.^[45]^[46] At its core, ROCm provides the Heterogeneous-compute Interface for Portability (HIP), a programming model that serves as an open alternative to NVIDIA's CUDA, enabling developers to write portable code for AMD GPUs with minimal modifications. This portability is achieved through HIP's ability to compile to either AMD's runtime or CUDA, facilitating easier migration of existing CUDA applications to AMD hardware. Key components of ROCm include runtime libraries such as the HIP runtime for kernel execution and management, which handle device code compilation, memory allocation, and parallel computation dispatch on AMD GPUs. The platform also offers native support for major machine learning frameworks, including PyTorch, TensorFlow, and JAX, allowing seamless integration for AI model training and inference without custom backends.^[47] For multi-GPU orchestration, ROCm incorporates the ROCm Communication Collectives Library (RCCL), which provides collective communication primitives like all-reduce and broadcast operations optimized for scaling across multiple nodes in distributed systems. ROCm is specifically optimized for AMD's CDNA (Compute DNA) architectures, which power the Instinct series accelerators, ensuring efficient utilization of specialized compute units for matrix operations and high-bandwidth memory access. In the MI300 series APUs, ROCm enables unified memory addressing, where CPU and GPU share a single high-bandwidth memory pool (up to 128 GB of HBM3), eliminating explicit data transfers and simplifying programming for memory-intensive applications like large-scale simulations.^[48] Notable development milestones include the ROCm 5.0 release in February 2022, which expanded hardware support for Radeon Pro GPUs and laid groundwork for broader ecosystem integration, followed by ROCm 5.7 in November 2023 introducing preview Windows support for select consumer GPUs via the HIP SDK.^[49] More recently, ROCm 7.0, released in September 2025, provides full support for the Instinct MI350 series with enhancements for low-precision formats like FP8, enabling faster AI inference and training on CDNA 4 architecture. ROCm 7.1 further improves MI350 support with optimizations in libraries like hipBLASLt for FP8 operations.^[50]^[46] The ROCm ecosystem has grown substantially, with widespread adoption in supercomputing; for instance, it powers the Frontier exascale supercomputer at Oak Ridge National Laboratory, where AMD Instinct MI250X GPUs achieve over 1.1 exaFLOPS for scientific simulations in climate modeling and drug discovery.^[51] By 2025, AMD reports a thriving developer community actively contributing through GitHub repositories and integrations with tools like Docker and Kubernetes, fostering open innovation in AI and HPC.^[52] Libraries such as MIOpen for deep learning operations are built directly atop the ROCm stack to leverage its runtime and portability features.

MIOpen library

MIOpen is AMD's open-source deep learning primitives library designed for high-performance machine learning operations on Instinct GPUs, serving as the ROCm equivalent to NVIDIA's cuDNN. It provides optimized implementations of core deep learning operators, including convolutions, recurrent neural networks (RNNs), activations, pooling, and normalization functions, enabling efficient training and inference of neural networks.^[53]^[54] A key feature of MIOpen is its auto-tuning capability, which dynamically selects the best kernel configurations for specific hardware and input sizes to maximize performance, alongside support for kernel fusion that combines multiple operations into single kernels to minimize memory accesses and reduce GPU launch overheads. This fusion optimization is particularly effective for memory-bound workloads, improving overall throughput in convolutional neural networks (CNNs). MIOpen relies on the ROCm runtime for low-level GPU interactions.^[53]^[55] MIOpen's evolution has aligned with ROCm releases and Instinct hardware advancements; early versions, such as those integrated with ROCm 2.0 in late 2018, provided initial support for the MI25 accelerator, enabling deep learning acceleration on Vega-based GPUs. Subsequent updates, including version 2.0 in 2019, introduced improvements like enhanced convolution performance and optional dependencies for greater flexibility. By 2025, with ROCm 7.0 and later, MIOpen supports advanced features optimized for the MI350 series on CDNA 4 architecture.^[56]^[15]^[46] As a backend, MIOpen integrates seamlessly with popular frameworks such as Apache MXNet and ONNX Runtime, facilitating model portability and execution on AMD hardware. Performance comparisons indicate that MIOpen delivers competitive results against cuDNN in common operations, often achieving parity or close to 90% of CUDA-equivalent throughput in convolution-heavy workloads on equivalent hardware, though outcomes vary by specific kernel and optimization level.^[57]^[58]^[59] MIOpen leverages unique optimizations tailored to Instinct's CDNA architecture, including efficient utilization of matrix cores for tensor operations and support for low-precision computing formats like FP8 and BF16, which enhance inference efficiency for large language models (LLMs) by reducing compute intensity while maintaining accuracy. These features enable higher matrix core occupancy in mixed-precision scenarios, contributing to breakthroughs in AI scalability.^[60]^[61] By 2025, MIOpen underpins a substantial portion of ROCm-based AI workloads, serving as a foundational component in enterprise deployments and playing a critical role in AMD's MLPerf submissions for the MI300X, where it helped achieve competitive inference and training results for models like Llama 2 70B, demonstrating strong scalability across multi-GPU configurations.^[62]^[63]^[64]

MxGPU virtualization

MxGPU is AMD's hardware-based GPU virtualization technology that leverages Single Root I/O Virtualization (SR-IOV) to enable secure sharing of a single physical GPU among multiple virtual machines (VMs).^[65] Introduced in 2016 with the FirePro S-Series professional graphics cards, it was later extended to the AMD Instinct lineup of data center accelerators, allowing up to 16 virtual GPUs (vGPUs) per physical Instinct card by presenting virtual functions (VFs) on the PCIe bus.^[66] This partitioning supports efficient resource allocation for compute-intensive workloads in virtualized environments, such as cloud data centers.^[67] In implementation, MxGPU divides the physical GPU's compute units, memory, and other resources into isolated vGPUs, with configurable profiles that allocate fractions of the total capacity (e.g., 1/4 or 1/16 of the GPU) to each VM.^[66] Quality of Service (QoS) controls ensure workload isolation by enforcing bandwidth limits and priority scheduling, preventing resource contention in multi-tenant setups.^[67] The technology integrates within the ROCm platform for seamless operation on Instinct hardware.^[65] MxGPU has evolved alongside Instinct accelerators and ROCm software updates. Enhancements in ROCm 4.0, released in 2021, improved support for the MI200 series by enabling features like live VM migration while maintaining GPU session continuity. By 2025, MxGPU continues to support Instinct series GPUs, including the MI350 series, for flexible partitioning in high-density deployments.^[68] Key use cases for MxGPU on Instinct include virtual desktop infrastructure (VDI) for AI developers, enabling multiple users to access GPU-accelerated environments simultaneously, and multi-tenant inference services in hyperscalers like Microsoft Azure.^[69] This sharing model reduces operational costs by up to 4x compared to dedicated GPU instances, as a single physical card serves multiple workloads efficiently.^[70] Security features emphasize isolation and protection, with SR-IOV providing independent memory spaces, interrupts, and DMA streams for each vGPU to minimize interference between VMs.^[71] Input-Output Memory Management Unit (IOMMU) support on the host enforces secure direct memory access, while per-vGPU profiling via the AMD System Management Interface (SMI) allows monitoring of metrics like utilization and temperature to detect and prevent anomalous behavior.^[72]^[73]

Product comparison

Specifications overview

The AMD Instinct series of accelerators has evolved significantly in terms of compute density, memory capacity, and power efficiency, driven by advancements in AMD's CDNA architectures and process nodes. Key specifications across models are summarized in the table below, drawing from official AMD documentation and verified benchmarks. This overview highlights the progression from early GCN-based designs to the latest CDNA 4-based GPUs optimized for AI and HPC workloads.^[12]^[1]

Model	Architecture	Process Node	Compute Units (SPs)	Memory (Type/Size/Bandwidth)	Peak Performance (FP64/FP32/FP16 TFLOPS)	TDP (W)	Release Year
MI6	GCN 1.2 (Hawaii)	28 nm	28 (1792)	HBM1 / 4 GB / 512 GB/s	0.21 / 4.2 / 8.4	150	2016
MI8	GCN 1.2 (Fiji)	28 nm	64 (4096)	HBM1 / 4 GB / 512 GB/s	0.51 / 8.2 / 16.4	175	2017
MI25	Vega 10	14 nm	64 (4096)	HBM2 / 16 GB / 484 GB/s	0.77 / 12.3 / 24.6	300	2017
MI100	CDNA 1 (Arcturus)	7 nm	120 (7680)	HBM2 / 32 GB / 1.23 TB/s	11.5 / 23.1 / 184.6 (matrix)	300	2020
MI210	CDNA 2 (Aldebaran)	7 nm	104 (6656)	HBM2e / 64 GB / 1.6 TB/s	34.7 / 69.5 / 139 (matrix)	250	2022
MI250X	CDNA 2 (Aldebaran)	7 nm	220 (14048)	HBM2e / 128 GB / 3.2 TB/s	47.9 / 95.7 / 383 (matrix)	560	2021
MI300A	CDNA 3 (Navajo)	5 nm	228 (14592)	HBM3 / 128 GB / 5.3 TB/s	61.3 / 122.7 / 2453 (matrix w/ sparsity)	760	2023
MI300X	CDNA 3 (Navajo)	5 nm	304 (19456)	HBM3 / 192 GB / 5.3 TB/s	81.4 / 163 / 2611 (matrix w/ sparsity)	750	2023
MI325X	CDNA 3 (Navajo)	5 nm	304 (19456)	HBM3e / 256 GB / 6 TB/s	81.7 / 163.4 / 2610 (matrix w/ sparsity)	1000	2024
MI350X	CDNA 4	3 nm	256 (16384)	HBM3e / 288 GB / 8 TB/s	72.1 / 144.2 / 2300 (matrix)	1000	2025
MI355X	CDNA 4	3 nm	256 (16384)	HBM3e / 288 GB / 8 TB/s	78.6 / 157.3 / 2500 (matrix w/ sparsity)	1400	2025

The series demonstrates a clear trend toward higher memory capacities and bandwidths to support large-scale AI models, evolving from the MI25's 16 GB HBM2 at 484 GB/s on a 14 nm process to the MI355X's 288 GB HBM3E at 8 TB/s on a 3 nm process, enabling handling of models with trillions of parameters. Compute performance has scaled dramatically, with FP64 throughput increasing from under 1 TFLOPS in early models to 81.7 TFLOPS in recent models like the MI325X, while TDP has risen to sustain higher sustained workloads, as verified in MLPerf benchmarks for AI training and inference.^[2]^[74] Notable generational leaps include the MI300X offering 1.7x the FP64 performance of the MI250X (81.4 TFLOPS vs. 47.9 TFLOPS) alongside 50% more memory (192 GB vs. 128 GB HBM3 vs. HBM2e), facilitating larger dataset processing in HPC applications.^[75] The MI350X further advances efficiency, achieving up to 35x leap in inferencing performance over previous generations, as demonstrated in official benchmarks emphasizing matrix operations for deep learning.^[38]^[30] These improvements are corroborated by MLPerf results, where Instinct accelerators have shown competitive throughput in large language model training and inference scenarios.^[63]