GPU Models

80GB · Hopper · The H100 SXM targets large-scale AI training workloads, particularly for language models up to 70 billion parameters where its 80GB memory capacity and high memory bandwidth prove essential. Its 990 TFLOPS FP16 performance and Transformer Engine make it well-suited for training and fine-tuning transformer-based models, while the substantial CUDA core count supports traditional HPC simulations and scientific computing. The MIG capability enables cloud providers to partition the GPU for multiple concurrent workloads, making it valuable for multi-tenant AI inference serving and development environments.

80GB · Ampere · The A100 SXM is well-suited for AI training workloads requiring substantial memory capacity, particularly large language models and computer vision tasks that benefit from the 80GB memory configuration. Deep learning inference applications with high throughput requirements can leverage the 312 TFLOPS FP16 performance and 624 TOPS INT8 capability. High-performance computing applications in scientific research, financial modeling, and data analytics benefit from the combination of CUDA cores and memory bandwidth. Multi-tenant cloud environments can utilize MIG technology to partition the GPU into smaller instances, maximizing resource utilization while maintaining workload isolation.

141GB · Hopper · The H200 is designed for memory-intensive AI workloads, particularly large language model training and inference where the 141GB HBM3E memory capacity enables handling of models that exceed the memory limits of previous generations. Its high memory bandwidth of 4.8TB/s makes it suitable for generative AI applications, recommendation systems, and natural language processing tasks that require rapid access to large datasets. The substantial Tensor Core count and FP16 performance capabilities also position it for AI training workflows, scientific computing applications, and high-performance computing tasks that can leverage its memory subsystem advantages.

48GB · Ada Lovelace · The L40S is well-suited for organizations requiring combined AI and graphics capabilities in cloud environments. Its 48GB memory capacity and Transformer Engine make it effective for large language model inference, generative AI applications, and medium-scale training workloads. The inclusion of RT Cores and DLSS 3 support enables professional rendering, architectural visualization, and content creation workflows. The GPU's 24/7 data center design makes it appropriate for production AI inference services, while its dual-purpose nature serves environments running NVIDIA Omniverse for collaborative 3D workflows alongside AI applications.

192GB · Blackwell · The B200 is designed for large-scale AI training and inference workloads that require substantial memory capacity and compute throughput. Its 192 GB VRAM makes it suitable for training large language models, processing extensive recommendation system datasets, and running memory-intensive scientific computing applications. The high INT8 performance and FP8 Tensor Core support optimize it for AI inference scenarios, while the substantial FP16 capability handles training workloads effectively. Organizations deploying chatbots, large language models, or complex AI pipelines benefit from the B200's combination of memory capacity and computational performance, particularly when workloads exceed the capabilities of lower-tier accelerators.

48GB · Ampere · The RTX A6000 addresses professional workloads requiring substantial memory capacity and mixed graphics-compute capabilities. Its 48GB GDDR6 memory and ECC support make it suitable for large-scale CAD modeling, architectural visualization, and scientific simulation where data integrity matters. The combination of RT cores and Tensor cores enables real-time ray tracing in content creation pipelines alongside AI-accelerated rendering workflows. In machine learning contexts, the substantial memory capacity supports training of moderately-sized models or inference on large models that exceed the memory limits of consumer GPUs, while the NVLink capability allows scaling to 96GB for even larger workloads.

96GB · Blackwell · The RTX 6000 Pro suits AI development and data science workflows requiring large memory capacity, particularly for training neural networks with substantial parameter counts or processing high-resolution datasets. The 96GB memory buffer supports computer graphics applications including ray tracing, 3D rendering, and CAD workflows where complex scenes exceed typical GPU memory limits. Video content creation benefits from the 9th Generation NVENC for streaming and encoding tasks, while the MIG capability enables multiple users or applications to share GPU resources efficiently in cloud workstation environments.

40GB · Ampere · The A100 PCIe is suited for AI training workloads requiring substantial memory capacity, particularly for natural language processing models, computer vision training, and recommendation systems that benefit from the 40GB memory buffer. Its Multi-Instance GPU capability makes it effective for inference serving scenarios where multiple smaller models can run simultaneously on partitioned resources. High-performance computing applications including scientific simulations, computational fluid dynamics, and molecular modeling leverage its FP32 and FP64 compute capabilities, while the PCIe form factor ensures compatibility with existing data center infrastructure without requiring specialized NVLink fabric investments.

40GB · Ada Lovelace · The L40 is well-suited for professional workloads that demand large memory capacity and mixed compute requirements. Its 48GB of ECC memory makes it appropriate for training medium to large AI models, running inference on memory-intensive models, and supporting virtualized workstation environments where multiple users share GPU resources. The combination of RT Cores and substantial VRAM supports 3D rendering, architectural visualization, and content creation workflows. Data science applications benefit from the large memory for processing extensive datasets, while the enterprise-grade design supports 24x7 cloud deployment scenarios requiring reliability and security features.

24GB · Ampere · The RTX A5000 is suited for professional visualization workflows including CAD rendering, engineering simulations, and architectural visualization where the 24GB ECC memory provides reliability and capacity for complex models. Its Tensor cores and substantial memory make it appropriate for moderate-scale AI model training and inference, particularly for computer vision and neural rendering applications. The professional driver certification and RTX Virtual Workstation support make it suitable for virtualized design environments and multi-user workstation deployments where GPU sharing is required.

32GB · Volta · The Tesla V100 is well-suited for entry-level AI development, smaller-scale model training, and traditional HPC workloads. Its 32GB memory capacity handles medium-sized datasets and models that don't require the latest architectural optimizations. The V100 works effectively for AI inference deployments, data science workflows, and scientific computing applications like molecular dynamics simulations. Organizations transitioning from CPU-based computing or exploring GPU acceleration for the first time often find the V100's balance of capability and accessibility appropriate for initial deployments.

24GB · Ada Lovelace · The RTX 4090 is well-suited for high-performance gaming servers, game development environments, and content creation workloads requiring substantial VRAM and graphics performance. Its 24GB memory buffer makes it capable of handling large 3D rendering tasks, video editing with high-resolution footage, and moderate-scale AI inference applications. The GPU's DLSS 3 capabilities make it particularly valuable for real-time ray tracing applications and gaming workloads where frame generation can enhance performance. With 82.6 TFLOPS of compute performance and 4th-generation Tensor cores, it can also serve AI development and inference tasks that don't require the specialized features of data center GPUs.

48GB · Ada Lovelace · The RTX 6000 Ada targets professional visualization, 3D rendering, and content creation workflows that benefit from its 48GB memory capacity and graphics acceleration features. Its combination of CUDA cores, RT Cores, and substantial VRAM makes it well-suited for architectural visualization, product design, video editing, and moderate-scale AI development. The ECC memory support and professional drivers also make it appropriate for technical computing applications requiring data integrity, while the AV1 encoding capabilities support modern video streaming and content creation pipelines.

48GB · Ampere · The A40 is well-suited for AI model training and data science workflows that require substantial VRAM capacity, particularly models with large parameter counts or extensive datasets that benefit from the 48GB memory buffer. Professional graphics applications, including CAD, content creation, and scientific visualization, leverage the RT Cores and high memory bandwidth. Virtual workstation deployments benefit from vGPU software support, enabling multiple concurrent users. The combination of traditional compute performance and AI acceleration makes it appropriate for mixed workloads in research environments and development workflows that span both graphics and machine learning requirements.

288GB · Blackwell · The HGX B300 is designed for large-scale generative AI model training, particularly for organizations developing or fine-tuning large language models that require substantial memory capacity and computational throughput. Its 288 GB VRAM and 5,000 TFLOPS of FP16 performance make it suitable for training transformer models, conducting complex scientific simulations, and processing large-scale data analytics workloads. The platform's multi-GPU architecture and high-bandwidth interconnects support distributed training scenarios and HPC applications that benefit from parallel processing across multiple accelerators.

24GB · Ada Lovelace · The L4 is well-suited for AI inference deployments requiring substantial memory capacity within power-constrained environments. Its 24GB memory buffer makes it appropriate for deploying medium-sized language models, computer vision applications processing high-resolution imagery, and video analytics workloads that benefit from keeping large datasets in GPU memory. The low 72-watt power envelope and compact form factor make it suitable for edge AI deployments, telecommunications infrastructure, and cloud providers seeking to maximize inference throughput per rack unit while minimizing cooling costs.

192GB · CDNA 3 · The MI300X suits large-scale generative AI training and inference tasks that require substantial memory capacity, particularly for large language models and multimodal AI applications. Its 192 GB memory configuration accommodates models that exceed the capacity of smaller accelerators, while the 5.3 TB/s memory bandwidth supports memory-bound workloads. The accelerator also targets high-performance computing applications including scientific simulations, computational fluid dynamics, and molecular modeling that benefit from high FP32 and FP64 performance combined with large memory capacity.

16GB · Ampere · The RTX A4000 is well-suited for professional graphics workloads, 3D rendering, and CAD applications that benefit from its 16GB of ECC memory and ray tracing capabilities. The substantial VRAM makes it appropriate for handling large models and datasets in engineering simulations or architectural visualization. Its Tensor cores enable AI inference workloads and machine learning model training at small to medium scales, while the professional driver stack provides certified compatibility with design software. The single-slot form factor and 140W power draw make it suitable for deployments where space and power constraints are considerations.

24GB · Ampere · The A30 is well-suited for AI inference serving that requires substantial memory capacity, medium-scale AI training workloads, and HPC applications that can leverage GPU acceleration. Its 24GB memory makes it capable of handling large language models and computer vision tasks that exceed the memory limits of smaller GPUs. The MIG functionality makes it particularly valuable in multi-tenant cloud environments where GPU resources need to be shared among multiple users or applications while maintaining isolation. Data analytics workloads involving large datasets benefit from the combination of memory capacity and compute performance.

94GB · Hopper · The H100 NVL is suited for large language model training and inference where its 94 GB memory capacity and Transformer Engine optimization provide advantages for transformer-based architectures. Its substantial compute capability makes it appropriate for high-performance computing applications requiring significant parallel processing power, while the Multi-Instance GPU feature enables cloud providers to partition resources for multiple tenants. The built-in Confidential Computing capabilities make it suitable for secure AI processing scenarios, and the balanced power consumption profile works well in data centers with thermal constraints while still requiring substantial AI compute capability.

24GB · Ampere · The RTX 3090 is well-suited for AI inference tasks requiring large memory capacity, 3D rendering and animation workloads, and content creation applications. Its 24 GB VRAM makes it capable of handling large language models and computer vision tasks that exceed the memory limits of smaller GPUs. The combination of substantial CUDA core count and Tensor core acceleration supports mixed-precision training for medium-scale deep learning projects, while RT cores enable hardware-accelerated ray tracing for rendering applications.

24GB · Ampere · The A10 is well-suited for hybrid workloads that combine professional graphics and AI inference, making it ideal for virtual desktop infrastructure serving CAD applications, architectural visualization, and content creation workflows. Its 24GB memory capacity and Tensor core capabilities support AI inference for computer vision, natural language processing, and recommendation systems at moderate scale. The GPU's support for NVIDIA vGPU software makes it particularly valuable in multi-tenant cloud environments where GPU resources are shared across multiple users running graphics-intensive applications or AI workloads that don't require the full compute power of larger data center GPUs.

96GB · Hopper · The GH200 is designed for giant-scale AI applications that require processing terabytes of data, particularly large language models, retrieval-augmented generation systems, and graph neural networks where its 96GB unified memory and coherent CPU-GPU architecture eliminate data movement bottlenecks. Its high memory bandwidth of 4 TB/s makes it suitable for HPC simulations, scientific computing, and data analytics workloads that are memory-bound rather than compute-bound. The superchip architecture is particularly effective for applications that benefit from tight CPU-GPU integration, such as complex AI pipelines that combine traditional computing with neural network inference.

80GB · Hopper · The H100 PCIe is well-suited for large language model training and inference, particularly for organizations requiring substantial memory capacity and Transformer Engine acceleration. Its 80GB memory makes it appropriate for fine-tuning large foundation models, running inference on billion-parameter models, and handling complex AI research workloads. The PCIe form factor makes it accessible for standard server deployments where enterprises need enterprise-grade AI performance without specialized interconnect infrastructure. High-performance computing applications benefit from its computational throughput, while the MIG capability allows cloud providers to partition the GPU for multiple concurrent workloads.

8GB · Ampere · The RTX 3070 serves cloud deployments requiring moderate GPU compute power, including AI inference for computer vision models, real-time ray tracing applications, and graphics rendering workloads. Its 8 GB VRAM capacity and Tensor Core acceleration make it suitable for deploying pre-trained neural networks, particularly in scenarios where the latest data center GPUs would be excessive. The GPU handles game streaming services, 3D rendering tasks, and development environments where users need graphics acceleration without enterprise-grade specifications.

16GB · Ampere · The A2 is optimized for AI inference applications in edge computing environments and scenarios requiring dense GPU deployments. Its 16GB memory capacity and Tensor core acceleration make it suitable for computer vision models, natural language processing inference, and text-to-speech applications that don't require the computational power of higher-tier GPUs. The low power consumption and compact form factor enable deployment in edge servers, retail environments, and distributed inference architectures where space and power are constrained. Organizations running multiple concurrent inference workloads can deploy several A2 GPUs in a single server due to their minimal thermal and power requirements.

288GB · CDNA 4 · The MI355X is designed for large-scale AI training and inference workloads that benefit from its 288GB memory capacity, including training transformer models, large language models, and computer vision networks that exceed the memory limits of smaller accelerators. The 8TB/s memory bandwidth and MXFP4 support make it effective for high-throughput inference serving. In HPC environments, the 78.6 TFLOPs FP64 performance and sparse matrix optimizations suit computational fluid dynamics, molecular dynamics simulations, and other scientific computing applications requiring both high memory capacity and compute precision.

10GB · Ampere · The RTX 3080 is well-suited for cloud gaming services requiring high-performance 4K gaming capabilities, virtual desktop infrastructure for creative professionals working with 3D rendering and video editing applications, and AI inference workloads that can benefit from its Tensor cores and 10GB VRAM capacity. Its ray tracing capabilities make it appropriate for architectural visualization, product design workflows, and real-time rendering applications. The GPU also serves development and testing environments for gaming applications and AI models where the latest generation hardware is not required.

12GB · Ampere · The RTX 3080 Ti excels in applications requiring high-performance GPU compute with substantial VRAM capacity. Its 12 GB memory makes it suitable for AI model training on medium-scale datasets, 3D rendering workflows, and video editing tasks involving 4K or higher resolution content. The card's 10,240 CUDA cores and Tensor core support enable efficient machine learning inference, computer vision processing, and scientific computing workloads. Content creators benefit from hardware-accelerated encoding, ray tracing capabilities for realistic rendering, and AI-enhanced features through NVIDIA's software ecosystem.

24GB · Ampere · The RTX 3090 Ti suits AI development and research scenarios where the 24 GB VRAM capacity enables working with larger language models, computer vision datasets, or deep learning experiments that exceed the memory limits of smaller GPUs. Content creation workflows benefit from the combination of substantial memory, RT Cores for ray tracing, and CUDA cores for rendering acceleration. The GPU handles game development tasks, 3D modeling, and video production where the large memory buffer reduces the need for asset streaming. Research applications in machine learning, particularly those requiring fine-tuning of medium-scale models or batch processing of datasets, can utilize both the memory capacity and Tensor Core acceleration.

20GB · Ada Lovelace · The RTX 4000 Ada targets professional workloads requiring both graphics and compute capabilities, particularly in space-constrained cloud deployments. Its 20GB VRAM capacity suits 3D modeling and rendering applications that handle complex scenes, while the fourth-generation Tensor cores enable AI inference tasks for content creation workflows. The hardware AV1 encoders make it suitable for video processing and streaming applications requiring modern compression standards. The single-slot design and 130W power envelope allow cloud providers to achieve higher GPU density per server while supporting workloads that benefit from the combination of RT cores for ray tracing, substantial memory capacity, and moderate compute performance.

12GB · Ada Lovelace · The RTX 4070 Ti is well-suited for cloud workloads requiring substantial graphics processing power and moderate AI capabilities. Its 12GB VRAM makes it appropriate for 3D rendering, video processing, and content creation workflows. The DLSS 3 support and Tensor cores enable AI-enhanced graphics applications and moderate machine learning inference tasks. The GPU's gaming heritage makes it particularly effective for cloud gaming services, virtual desktop infrastructure requiring graphics acceleration, and development environments for graphics-intensive applications. The balance of performance and power efficiency makes it suitable for medium-scale deployments where high-end datacenter GPUs would be excessive.

16GB · Ada Lovelace · The RTX 4080 suits cloud workstations running graphics-intensive creative applications, game development environments, and AI-enhanced content creation workflows. Its 16GB memory capacity handles large 3D scenes, high-resolution video editing, and machine learning inference tasks effectively. The dedicated ray tracing hardware accelerates architectural visualization, product design rendering, and real-time graphics applications. DLSS 3 support makes it valuable for cloud gaming services and remote workstations where performance optimization is critical. The GPU also serves AI researchers and developers working on computer vision, image processing, and generative AI applications that benefit from its Tensor core acceleration.

64GB · Ampere · The A16 is optimized for virtual desktop infrastructure deployments where multiple users require dedicated graphics resources. Its quad-GPU design and 64GB total VRAM make it well-suited for VDI providers serving knowledge workers, designers using CAD applications, or organizations running graphics-rich virtual desktops. The card's video encoding capabilities and support for up to 64 concurrent users make it effective for virtual workstation environments, remote work scenarios, and educational institutions requiring scalable graphics virtualization. The Tensor Cores also enable AI inference workloads in virtualized environments, though the A16 is not positioned for large-scale training tasks.

576GB · Blackwell · The GB300 is designed for hyperscale AI factory applications that require maximum computational throughput and memory capacity. Its 576 GB memory and AI Reasoning Inference capabilities make it well-suited for large language model inference, real-time video generation, and test-time scaling workloads where models need extensive memory for processing complex reasoning tasks. The rack-scale architecture and high interconnect bandwidth support distributed inference across multiple models simultaneously, making it appropriate for cloud providers offering premium AI services or research institutions running large-scale AI experiments that demand the highest available performance tier.

256GB · CDNA 3 · The MI325X is designed for large-scale AI training and inference workloads that require substantial memory capacity, making it suitable for training large language models, computer vision networks, and other memory-intensive AI applications. The 256 GB HBM3E memory enables processing of datasets and models that exceed typical GPU memory limits, while the high FP8 and FP16 performance supports both training and inference phases. Scientific computing and high-performance computing workloads benefit from the native FP64 support delivering 81.7 TFLOPs, making it applicable for computational fluid dynamics, molecular modeling, and financial simulations. The Infinity Fabric interconnect and OAM form factor make it well-suited for multi-GPU deployments in enterprise data centers requiring high computational density.

8GB · Ampere · The RTX 3070 Ti suits cloud deployments focused on gaming services, content creation, and moderate AI inference workloads. Its 8GB GDDR6X memory and 6,144 CUDA cores provide sufficient resources for 1440p game streaming, video editing applications, and AI models that fit within its memory constraints. The 3rd Generation Tensor Cores enable mixed-precision training for smaller neural networks, while 2nd Generation RT Cores support real-time ray tracing in graphics applications. Cloud providers often deploy RTX 3070 Ti instances for customers requiring GPU acceleration without the cost premium of professional datacenter GPUs.

8GB · Ada Lovelace · The RTX 4060 Ti serves cloud gaming instances, virtual desktop infrastructure, and moderate content creation workloads where its 8GB VRAM and 22.06 TFLOPS performance provide adequate acceleration without the overhead of professional cards. Its 160W power envelope makes it suitable for deployments where energy efficiency is important, while DLSS 3 and RT core capabilities enable modern gaming experiences and real-time rendering tasks. Development environments benefit from its balance of performance and cost, particularly for graphics programming, game development, and moderate AI inference tasks that don't require the memory capacity or compute density of data center GPUs.

12GB · Ada Lovelace · The RTX 4070 suits cloud workloads requiring consumer-grade GPU acceleration with moderate compute demands. Its 12GB VRAM and Ada Lovelace architecture make it appropriate for gaming instances, virtual desktop infrastructure, light AI inference applications, and content creation workflows. The GPU's DLSS 3 and RT core capabilities benefit applications specifically designed to leverage these gaming-focused technologies, while its 200W power envelope makes it suitable for deployments where power efficiency is prioritized over maximum compute throughput.

32GB · Ada Lovelace · The RTX 5000 is well-suited for professional visualization workloads including 3D modeling, rendering, and simulation applications that benefit from its 32GB memory capacity and RT core acceleration. The fourth-generation Tensor cores and substantial AI compute performance make it effective for generative AI workflows, inference applications, and machine learning development where the large memory buffer supports complex models. Content creators can leverage the AV1 encoders for efficient video processing, while the ECC memory support provides reliability for mission-critical professional applications that require data integrity.

6GB · Ampere · Light AI inference, CAD/CAM visualization, entry-level ML development

16GB · Turing · The Tesla T4 is well-suited for AI inference workloads that require moderate computational power, such as computer vision applications, natural language processing with smaller models, and real-time recommendation systems. Its dedicated transcoding engines make it effective for video analytics pipelines that combine AI inference with media processing. The 16 GB memory capacity accommodates models up to medium complexity, while the 70-watt power envelope enables deployment in edge computing scenarios or data centers with strict power budgets. Organizations using the T4 typically run inference-focused workloads rather than training, leveraging its multi-precision capabilities for INT8 and FP16 optimized models.

96GB · Gaudi · The Gaudi 2 is well-suited for large language model training and inference, multi-modal AI applications, and enterprise RAG deployments that require substantial memory capacity and bandwidth. With 96GB of memory and 432 TFLOPS of FP16 performance, it handles memory-intensive workloads effectively. The Ethernet-based scaling makes it particularly attractive for organizations with existing network infrastructure who want to avoid proprietary interconnect investments. Its high memory bandwidth of 2,450 GB/s supports applications where model parameters and activations exceed typical GPU memory limits, making it suitable for training large transformer models and running inference on models that require significant memory footprints.

384GB · Blackwell · The GB200 is designed for large-scale AI training and inference workloads that require substantial memory capacity and high inter-GPU bandwidth. Its 384GB VRAM makes it suitable for training and serving large language models, while the 72-GPU NVLink domain capability supports massive parallel training runs. The high memory bandwidth and FP4 precision support through the Transformer Engine optimize it for transformer-based models and large-scale inference serving. HPC applications requiring high memory capacity and low-latency GPU-to-GPU communication also benefit from its specifications.

8GB · Ada Lovelace · The RTX 4060 is designed for cloud gaming instances targeting 1080p and entry-level 1440p gaming, streaming workloads that benefit from AV1 encoding, and light content creation tasks. Its 8GB memory capacity and DLSS 3 support make it suitable for modern games at medium to high settings, while the Ada Lovelace architecture's media engines handle streaming and video encoding efficiently. The GPU works well for users learning game development, running CAD applications, or performing light 3D rendering tasks that don't require the computational power of professional workstation cards.

16GB · Ada Lovelace · The RTX 4070 Ti SUPER excels in applications requiring substantial VRAM capacity, making it well-suited for AI model training with medium-scale datasets, 3D rendering and animation projects, and high-resolution video editing workflows. Its 16 GB memory buffer allows it to handle neural networks and datasets that would exceed the capacity of 12 GB cards, while the 8,448 CUDA cores provide the computational throughput needed for parallel processing tasks. The card is also effective for virtual desktop infrastructure supporting graphics-intensive applications and development environments requiring GPU acceleration.

16GB · Ada Lovelace · The RTX 4080 SUPER excels in cloud gaming services where its 16 GB VRAM and DLSS 3 capabilities enable high-resolution gaming with enhanced frame rates. Its substantial compute performance makes it suitable for real-time rendering workflows, game development, and content creation tasks that leverage GPU acceleration. The combination of CUDA cores and Tensor cores supports AI inference applications, particularly those involving computer vision and real-time AI processing where the consumer-grade feature set provides adequate performance without professional GPU premiums.

24GB · Ada Lovelace · The RTX 4500 Ada excels in professional workstation environments requiring substantial GPU memory and compute performance. Its 24GB ECC memory makes it well-suited for generative AI model inference, large-scale 3D rendering projects, and data visualization workflows that benefit from keeping entire datasets in GPU memory. The AV1 encoding capabilities make it valuable for video production and streaming applications, while the RT cores accelerate ray tracing workloads in architectural visualization and product design. The combination of Tensor cores and professional driver optimization positions it effectively for AI development workflows and simulation tasks in engineering and scientific computing environments.

192GB · Blackwell · The B100 is designed for enterprise AI workloads requiring substantial memory capacity and compute performance. Its 192 GB VRAM makes it suitable for training and serving large language models, particularly trillion-parameter models that exceed the memory constraints of smaller GPUs. The high memory bandwidth and Ultra Tensor Cores optimize performance for generative AI applications, while NVLink scaling capabilities support distributed training across multiple nodes. The GPU also serves data analytics workloads that benefit from large memory capacity and the dedicated Decompression Engine for data processing acceleration.

128GB · Gaudi · The Gaudi 3 is designed for large language model training and inference, multi-modal AI applications, and enterprise retrieval-augmented generation (RAG) systems. Its 128 GB memory capacity makes it suitable for training large transformer models that require substantial memory for parameters and activations. The Ethernet-based fabric architecture makes it particularly well-suited for organizations that want to scale AI workloads using existing network infrastructure rather than investing in specialized interconnects. Enterprise deployments benefit from its standard form factors and infrastructure compatibility, while the high memory bandwidth supports both training workflows and high-throughput inference scenarios.

48GB · Xe HPC · The Max 1100 is well-suited for organizations invested in Intel's hardware ecosystem seeking GPU acceleration for AI and HPC workloads. Its 48 GB memory capacity makes it appropriate for training medium to large neural networks and running inference on models that exceed the memory limits of smaller GPUs. The substantial memory bandwidth and XMX engines support AI training tasks, while the Xe Vector Engines handle traditional HPC computations. The oneAPI support makes it particularly attractive for mixed workloads that span Intel CPUs and GPUs, providing a unified development environment.

128GB · Xe HPC · The Max 1550 targets high-performance computing and AI workloads that require large memory capacity and benefit from Intel's oneAPI ecosystem. Its 128 GB HBM2e makes it suitable for training large language models, scientific simulations with extensive datasets, and memory-intensive inference tasks. The substantial memory bandwidth supports data-parallel workloads, while the Intel XMX engines accelerate mixed-precision AI training and inference. Organizations already invested in Intel's software tools and seeking alternatives to NVIDIA's ecosystem may find the Max 1550 appropriate for compute clusters and AI development platforms.

32GB · CDNA · The MI100 is designed for high-performance computing workloads that benefit from its substantial 32GB memory capacity and strong FP64 performance capabilities. Scientific simulations, computational fluid dynamics, and molecular modeling applications can leverage the 11.5 TFLOPs of double-precision performance. Machine learning training workloads requiring large memory footprints can utilize the 1.2TB/s memory bandwidth and 184.6 TFLOPs of FP16 performance. The accelerator is also suitable for inference deployments where the 32GB memory allows hosting multiple large models simultaneously.

64GB · CDNA 2 · The MI210 is well-suited for enterprise HPC workloads requiring substantial memory capacity and double-precision compute performance, such as computational fluid dynamics, molecular dynamics simulations, and weather modeling. Its 64 GB VRAM makes it effective for AI training scenarios involving large language models or computer vision tasks with high-resolution datasets. Research institutions benefit from its combination of FP64 performance for scientific computing and FP16/bfloat16 capabilities for machine learning experiments. The multi-GPU scaling via Infinity Fabric Links supports distributed computing workloads that can utilize multiple accelerators in parallel.

128GB · CDNA 2 · The MI250 is designed for large-scale AI training and high-performance computing workloads that require substantial memory capacity and computational throughput. Its 128 GB memory makes it suitable for training large language models, computer vision models with high-resolution datasets, and scientific simulations that process large amounts of data. The high FP64 performance makes it particularly valuable for scientific computing applications in computational fluid dynamics, molecular dynamics, and weather modeling. Organizations using AMD's ROCm ecosystem or seeking alternatives to NVIDIA's platform will find the MI250 effective for distributed training across multiple nodes.

128GB · CDNA 2 · The MI250X excels in HPC workloads requiring substantial memory capacity and double-precision performance, including computational fluid dynamics, molecular dynamics simulations, and climate modeling. The 128 GB memory makes it suitable for training large AI models that exceed the capacity of smaller GPUs, while the high FP64 performance serves scientific applications demanding numerical precision. Multi-GPU configurations benefit from Infinity Fabric connectivity for applications requiring distributed memory across multiple accelerators.

128GB · CDNA 3 · The MI300A is designed for heterogeneous computing workloads that require both CPU and GPU processing with frequent data exchange. Its unified architecture makes it suitable for complex AI training pipelines with extensive data preprocessing, scientific simulations that alternate between scalar and parallel computations, and large language model training where the 128 GB unified memory can accommodate larger models without partitioning. The high memory bandwidth and capacity also benefit memory-bound HPC applications like computational fluid dynamics and molecular modeling.

12GB · Ada Lovelace · The RTX 4070 SUPER targets development and testing environments where moderate GPU acceleration is needed without data center GPU costs. Its 12GB VRAM makes it suitable for machine learning experimentation, 3D rendering tasks, and content creation workflows. The combination of CUDA cores and Tensor Cores supports mixed workloads including computer vision development, game development with ray tracing, and AI model prototyping. Cloud providers often deploy this GPU for users requiring dedicated graphics performance for creative applications, engineering simulations, or medium-scale parallel computing tasks.