As industries across the globe rely more heavily on data-intensive applications, GPUs have become essential for pushing the limits of what’s possible in the cloud. However not all GPUs are created equal—each family is built with specific use cases in mind, and Azure’s NG and NV families are prime examples of this specialization.
While NG GPUs are optimized for high-performance graphics and media workloads, the NV GPUs are designed to accelerate AI and deep learning tasks. Each family offers tailored power to meet the unique demands of industries ranging from gaming and media production to scientific research and AI development.
But with so many GPU options out there, especially compared to other cloud providers like AWS, you might wonder: Why choose Azure’s NG and NV families for your workloads? And what specific value do they bring to your business?
This blog will help you answer those questions. We'll dive deep into how NG and NV families are uniquely designed to support your needs—whether you’re building an immersive gaming experience, running complex simulations, or training AI models. By comparing the strengths of each family, we’ll show you how to choose the right one for your business and workload.
NG Family: Unmatched Performance for Graphics and Media
The NG Family of GPUs is optimized for industries that require high-quality graphics, real-time rendering, and interactive experiences. Whether you're working on gaming, media production, or immersive simulations, the NG series offers unparalleled performance for demanding visual tasks.
Key Strengths of the NG Family:
- Exceptional 3D Rendering: NG GPUs excel at rendering complex 3D environments with high visual fidelity and smooth performance, making them ideal for gaming developers and digital artists.
- Real-Time Media Optimization: With low latency and high frame rates, NG GPUs provide a seamless experience for real-time applications like live streaming, VR, and interactive media.
- Scalable Graphics Solutions: From animation studios producing high-resolution films to global streaming platforms, NG GPUs scale effortlessly to meet the demands of large-scale visual workloads.
Why NG GPUs Stand Out: Their ability to handle complex 3D rendering and high-resolution visual tasks at scale, combined with low-latency processing, sets them apart for real-time applications.
NV Family: Pioneering AI, Deep Learning, and HPC
The NV Family is purpose-built for compute-heavy workloads, powering industries at the forefront of AI, deep learning, and scientific research. NV GPUs provide the necessary computational power for data-intensive applications, driving breakthroughs in AI model training, large-scale simulations, and data analytics.
Core Advantages of the NV Family:
- Accelerated AI Training: NV GPUs leverage Tensor Cores to speed up AI model training, reducing time-to-insight from days to hours.
- High-Throughput for HPC: Designed to support high-performance computing, NV GPUs power complex simulations across industries like genomics, climate research, and material science.
- Scalable Compute Power: With massive parallel processing, NV GPUs excel at running AI models and simulations at scale, enhancing productivity in data-heavy fields.
Why NV GPUs Stand Out: Their unique architecture—specifically designed for parallel processing and deep learning—enables unparalleled speeds in AI training and scientific computing.
NG vs NV: The Key Differentiators
While both families offer high-performance solutions, the NG and NV GPUs are optimized for different use cases. Let's take a look at the key differences:
| Feature | NG Family | NV Family |
| GPU Models | NVIDIA T4, A40, A100 | NVIDIA A100, V100, H100 |
| Ideal Workloads | Graphics rendering, gaming, real-time video processing, VR/AR, media production | AI/ML model training, deep learning, high-performance computing (HPC), scientific simulations |
| Memory Type | Graphics-oriented memory (e.g., GDDR6) (16GB) | High-bandwidth memory (e.g., HBM2) (16GB to 80GB)for data-intensive tasks |
| Compute Performance | Optimized for real-time graphics rendering, video processing, and interactive workloads | Optimized for parallel computation, deep learning, and large-scale AI workloads |
| AI & Machine Learning | Not specialized for AI tasks | Specialized in accelerating AI/ML model training and inference |
| Parallel Processing | Limited parallel computing capacity for graphics | Strong parallel processing capabilities for AI, HPC, and scientific computing |
| Networking Support | Typically supports 25 Gbps (NVIDIA NVLink) | Supports up to 100 Gbps (NVIDIA NVLink, InfiniBand) |
| Power Consumption | ~70W (T4) | ~250W to 300W (A100, V100) |
| Scalability | Horizontal scalability for distributed media workloads | Vertical scalability for compute-heavy tasks requiring massive parallelism |
Hardware Innovations and Architectural Advances in Each Family
This section explores the distinct architectural innovations and hardware advancements of the NG Family and NV Family GPUs. While both families are designed for high-performance computing, they are optimized for different workloads
NG Family Architecture
The NG Family GPUs are designed to focus on real-time graphics rendering and interactive visual applications primarily. Key architectural innovations include:
- Ray Tracing Cores: Specialized cores dedicated to real-time ray tracing, enabling accurate simulation of lighting, shadows, and reflections in complex 3D environments. These cores significantly enhance the realism of rendered scenes.
- Shader Cores: These cores are optimized for managing shaders, essential for producing high-quality graphics. They support complex visual effects, real-time rendering, and gaming experiences by executing intensive graphical calculations.
- Low Latency Processing: Designed for minimal latency, NG GPUs ensure smooth, high-frame-rate rendering in visually intensive applications, providing seamless performance in gaming, VR, and interactive media.
- HD and Ultra-HD Output: NG Family GPUs support high-definition (HD) and 4K+ resolution, enabling high-quality output for content creation, media production, and immersive viewing experiences.
- Unified Architecture: The NG GPU architecture integrates various functions like 3D rendering, video transcoding, and simulation tasks, ensuring efficient performance across different visual workloads while balancing power consumption and performance.
This architecture is specifically built for high-fidelity visuals, capable of processing the intensive graphical requirements of modern media, rendering, and gaming experiences.
NV Family Architecture
The NV Family is optimized for demanding computational tasks such as AI model training and HPC. The architectural features of the NV GPUs focus on maximizing computational power and data throughput:
- Tensor Cores: These specialized cores are designed to accelerate AI operations such as matrix multiplications and convolutions, which are critical for deep learning and neural network training. Tensor cores enable high throughput and compute efficiency for AI workloads.
- High-Bandwidth Memory (HBM2): NV GPUs use HBM2 memory, providing extremely high memory bandwidth, which is essential for processing large datasets and performing high-speed computations in AI and scientific simulations.
- Streaming Multiprocessors (SMs): NV GPUs feature Streaming Multiprocessors optimized for handling massively parallel workloads. These multiprocessors are designed to handle the heavy computational demands of AI, ML, and HPC tasks.
- NVIDIA NVLink: NVLink facilitates high-speed interconnects between multiple GPUs, allowing for scalability in distributed AI research clusters and supercomputing environments. This feature enables efficient scaling of AI workloads across several GPUs without significant performance loss.
- Compute-Optimized Design: NV GPUs are built with a focus on compute efficiency, ensuring that large-scale AI training, deep learning, and HPC workloads can be processed efficiently, with reduced latency and maximized throughput.
This architecture is built to handle the massive parallelism required by modern AI and HPC workloads, delivering superior performance for data science and deep learning tasks.
| Feature | NG Family | NV Family |
| Low Latency Processing | Designed for minimal latency in gaming, VR, and interactive media | Not a primary focus |
| Resolution Support | HD and Ultra-HD, optimized for high-quality visual output | No specific focus on output resolution |
| Memory Type | GDDR6 (Graphics-Optimized) | HBM2 (High-Bandwidth for data-intensive tasks) |
| Compute Focus | Real-time graphics rendering and 3D simulation | AI/ML model training, HPC simulations |
| Parallel Computing Support | Moderate (Optimized for visuals) | High (Massively parallel for AI and HPC) |
| GPU Interconnects | No specific high-speed interconnects | Yes, supports NVIDIA NVLink for multi-GPU scaling |
| Power Consumption | Relatively lower power consumption (~70W, T4) | Higher power consumption (~300W, A100) |
Key Takeaways
- NG Family:
- Graphics-Centric Architecture: Designed for visual tasks with Ray Tracing Cores and Shader Cores for high-quality rendering.
- Memory: Utilizes GDDR6 optimized for graphical data and rendering workloads.
- Low Latency: Built for minimal latency, ensuring smooth performance in interactive media and real-time rendering.
- NV Family:
- Compute-Focused Architecture: Optimized for AI and machine learning with Tensor Cores accelerating deep learning operations.
- Memory: Equipped with HBM2 for high-bandwidth processing of large datasets.
- Latency: Designed to handle low-latency computing, crucial for AI inference and real-time data processing.
Performance Benchmarks
In this section, we’ll compare the NG and NV families based on key performance metrics: graphics rendering capabilities, compute power, and data throughput.
NG Family Performance
Performance benchmarks show outstanding capabilities in real-time rendering, video processing, and interactive media applications. Key performance metrics for NG Family GPUs include:
- 4K and Ultra-HD Rendering: The NG Family delivers exceptional performance in rendering high-resolution graphics. In benchmarks, NG GPUs can handle 4K video processing at high frame rates (60fps and above), making them ideal for media production, gaming, and VR.
- Real-Time Ray Tracing: NG GPUs, equipped with dedicated Ray Tracing Cores, show significant improvements in rendering realistic lighting, shadows, and reflections in complex 3D environments. Benchmarks reveal that the NG GPUs can maintain smooth frame rates even during intensive real-time ray tracing tasks in AAA gaming titles.
- Gaming and VR Performance: NG GPUs excel in delivering high frame rates in real-time gaming and VR experiences. Benchmarks show that NG GPUs can handle graphically intense VR environments with minimal latency, providing a seamless experience with high-quality visuals at stable frame rates.
NV Family Performance
The NV GPUs achieve unmatched performance in data-intensive applications. Key performance benchmarks for NV Family GPUs include:
- AI Model Training: The NV Family's Tensor Cores deliver massive acceleration in AI and machine learning workflows. In benchmarks, NV GPUs show significant reductions in training times for deep learning models, offering up to 5x faster AI training compared to standard GPUs.
- High-Throughput for AI Inference: For AI inference tasks, NV GPUs provide real-time processing for large AI models. Benchmarks highlight up to 30% faster inference times, enhancing the speed and efficiency of AI-driven applications.
- HPC and Simulation Performance: NV GPUs, equipped with HBM2 memory and designed for massive parallelism, outperform traditional GPUs in high-performance computing (HPC) workloads. Performance tests show NV GPUs processing large-scale simulations and scientific computations with up to 3x faster throughput than previous-generation GPUs.
| Feature | NG Family | NV Family |
| 4K/Ultra-HD Rendering | Excellent, handles high-fidelity 4K rendering at 60fps+ | N/A |
| Ray Tracing Performance | High performance in real-time ray tracing for realistic lighting and reflections | N/A |
| Gaming and VR | Seamless performance in real-time gaming and VR, with high frame rates and minimal latency | N/A |
| AI Model Training | N/A | Up to 5x faster AI model training with Tensor Cores |
| AI Inference | N/A | 30% faster inference for AI workloads |
| HPC Simulations | N/A | 3x faster performance for large-scale simulations |
Key Takeaways
- NG Family: Optimized for high-quality visuals and real-time rendering, NG GPUs deliver exceptional performance in graphics-intensive tasks like 4K rendering, gaming, and VR. The Ray Tracing Cores and shader optimization provide smooth, high-fidelity visuals for demanding graphical workloads.
- NV Family: Tailored for compute-intensive workloads, NV GPUs outperform in AI training, deep learning, and HPC simulations. With Tensor Cores and HBM2 memory, NV GPUs offer dramatically faster AI training and real-time inference processing, as well as significant acceleration in scientific simulations.
Scalability: Growing with Your Workload Needs
One of the key advantages of both the NG and NV families is their scalability, which allows organizations to grow their GPU infrastructure alongside their evolving needs.
NG Family Scalability
The NG Family GPUs are designed to excel in horizontal scaling, a strategy where multiple GPUs or nodes work together to handle distributed tasks. This approach is particularly beneficial for workloads that can be broken down into parallel tasks, such as real-time graphics rendering or media processing.
Why Horizontal Scaling?
- Parallelism in Visual Workloads: Real-time rendering tasks like gaming, VR, and video transcoding are well-suited for horizontal scaling because they can be split into smaller, independent tasks. Each GPU can handle a distinct portion of the workload, enabling distributed rendering across multiple nodes.
- Efficient Task Distribution: Horizontal scaling allows the NG family to efficiently distribute tasks across multiple GPUs, ensuring that each unit is fully utilized without overloading a single node.
- Reduced Bottlenecks: For workloads like 3D rendering or interactive media, where high concurrency is required, distributing the workload across GPUs or nodes helps avoid system bottlenecks. The low-latency communication between GPUs ensures that performance remains consistent across large-scale deployments.
Key Scalability Features
- Multi-Node Scaling: Seamlessly scales across multiple GPUs or nodes to distribute tasks. Ideal for workloads where tasks can be divided and processed concurrently.
- Parallel Task Distribution: Ensures tasks are spread out across GPUs to maintain high throughput without significant performance degradation.
- Low-Latency Communication: Focused on maintaining low-latency interaction between GPUs, ensuring minimal delay even when scaling horizontally.
NV Family Scalability
The NV Family is optimized for vertical scaling, which focuses on increasing the computational power within a single server by adding multiple GPUs. This is ideal for AI, machine learning (ML), and high-performance computing (HPC) tasks, where a large amount of computational power is required for a single task or dataset.
Why Vertical Scaling?
- Data-Intensive and Compute-Heavy Workloads: AI model training, scientific simulations, and large-scale data processing require GPUs that can process massive datasets in parallel. Vertical scaling within a single node ensures that all GPUs share the same high-bandwidth memory (HBM2) and can access large datasets efficiently.
- Efficient Memory Sharing: Vertical scaling is well-suited for workloads where GPUs need to share a unified memory pool. The NV family’s use of HBM2 memory and NVLink allows multiple GPUs to work together within a single server, improving memory throughput and reducing latency for data transfers.
- Low Latency for AI Tasks: AI and HPC workloads benefit from the reduced latency provided by vertically scaling within a node. Data does not need to travel between multiple nodes, which allows for faster computation in tasks like deep learning training.
Key Scalability Features
- Multi-GPU Scaling Within a Node: Supports scaling within a single server by adding multiple GPUs, making it ideal for tasks that need to process large datasets or compute-heavy operations.
- Efficient Data Sharing with NVLink: NVLink enables fast inter-GPU communication within a node, improving data throughput and minimizing bottlenecks when scaling vertically.
- Unified Memory Pool: HBM2 memory ensures that GPUs within a node have access to a shared memory pool, facilitating large-scale data processing with high efficiency.
| Scalability Feature | NG Family | NV Family |
| Scaling Approach | Horizontal scaling across multiple GPUs/nodes | Vertical scaling within a single node (multi-GPU) |
| Multi-Node Scalability | Yes, workloads can be distributed across multiple GPUs or nodes | Limited to within a single node (supports multiple GPUs in a node) |
| Task Distribution | Tasks are divided into smaller parallel tasks for concurrent processing across GPUs | Shared memory and resources within a single node for intensive computation |
| Inter-GPU Communication | Standard network protocols (Ethernet, InfiniBand) for inter-node communication | NVLink or PCIe for high-speed inter-GPU communication within a node |
| Bandwidth Scalability | Moderate, optimized for parallel processing of visual data across multiple GPUs | High, with shared access to HBM2 memory for massive throughput within a node |
| Power Scaling | More energy-efficient across multiple lower-power GPUs | Higher power consumption due to multi-GPU setups in a single node |
| GPU Interconnects | No specific high-speed interconnects beyond basic network connections between nodes | NVLink for high-speed inter-GPU communication and scaling within a node |
| Node Efficiency | Efficient for workloads that can be split across many nodes (e.g., rendering, media production) | Efficient for data-heavy, compute-intensive tasks like AI/ML training and simulations |
Cost Efficiency
Selecting the right VM series based on cost efficiency is essential for managing cloud expenses effectively. The NG Family and NV Family offer different pricing models based on the type of workload and the GPU requirements.
NG Family
The key VM series in this family, NGads V620, provides NVIDIA T4 GPUs, offering a cost-effective solution for businesses that need moderate GPU power for media and graphics tasks.
VM Series in NG Family:
- NGads V620 series: Uses NVIDIA T4 GPUs, optimized for GPU-accelerated graphics workloads.
NG Family Pricing Overview:
| VM Series | GPU Model | GPU Memory | On-Demand Price (Hourly) | 1-Year Reserved Price (Hourly) | Spot Price (Hourly) | Reserved Savings vs On-Demand |
| NGads V620 | NVIDIA T4 | 16 GB GDDR6 | $0.53 | $0.13 | $0.10 | ~75% |
Key Cost Insights for NG Family:
- On-Demand Pricing: The NGads V620 series starts at $0.53/hour, providing flexibility for businesses with fluctuating GPU needs.
- Reserved Instances: Opting for 1-year reserved instances offers significant savings of up to 75%, lowering the hourly rate to $0.13.
- Spot Instances: For non-critical tasks that can handle interruptions, spot instances provide up to 81% savings, reducing the cost to as low as $0.10/hour.
NV Family
The NV Family is optimized for AI/ML training, high-performance computing (HPC), and data-heavy simulations. The key VM series in this family, such as the NVadsA10_v5 series and NVv3 series, utilize NVIDIA A10G GPUs and NVIDIA M60 GPUs, respectively. These are designed to handle more compute-intensive tasks, which comes with a higher cost compared to the NG family.
VM Series in NV Family:
- NVv3 series: Powered by NVIDIA M60 GPUs, designed for both graphics and compute tasks.
- NVv4 series: Features AMD Radeon Pro V340 GPUs, designed for virtual desktops and remote workstations.
- NVadsA10_v5 series: Uses NVIDIA A10G GPUs, optimized for AI and machine learning workloads.
- NVads V710 v-5 series: Equipped with NVIDIA A100 GPUs, designed for high-performance AI/ML tasks.
NV Family Pricing Overview:
| VM Series | GPU Model | GPU Memory | On-Demand Price (Hourly) | 1-Year Reserved Price (Hourly) | Spot Price (Hourly) | Reserved Savings vs On-Demand |
| NVv3 series | NVIDIA M60 | 16 GB GDDR5 | $0.90 | $0.45 | $0.30 | ~50% |
| NVv4 series | AMD Radeon V340 | 32 GB GDDR5 | $1.20 | $0.60 | $0.45 | ~50% |
| NVadsA10_v5 | NVIDIA A10G | 24 GB GDDR6 | $1.10 | $0.55 | $0.40 | ~50% |
| NVads V710 v-5 | NVIDIA A100 | 40 GB HBM2 | $3.00 | $1.50 | $1.00 | ~50% |
Key Cost Insights for NV Family
- On-Demand Pricing: Pricing for NV Family instances starts at $0.90/hour for NVv3 series and goes up to $3.00/hour for the NVads V710 v-5 series, which reflects the high computational capabilities of the GPUs.
- Reserved Instances: 1-year reserved pricing can reduce the hourly rate by 50%, with prices as low as $0.45/hour for NVv3 series and $1.50/hour for NVads V710 v-5.
- Spot Instances: Spot pricing offers substantial savings, providing up to 67% savings for the NVads V710 v-5 series (down to $1.00/hour).
Cost Comparison: NG vs NV Family VMs
| VM Family | VM Series | GPU Model | On-Demand Price (Hourly) | 1-Year Reserved Price (Hourly) | Spot Price (Hourly) | Reserved Savings vs On-Demand |
| NG Family | NGads V620 | NVIDIA T4 | $0.53 | $0.13 | $0.10 | ~75% |
NV Family
| NVv3 series | NVIDIA M60 | $0.90 | $0.45 | $0.30 | ~50% |
| NVv4 series | AMD Radeon V340 | $1.20 | $0.60 | $0.45 | ~50% | |
| NVadsA10_v5 | NVIDIA A10G | $1.10 | $0.55 | $0.40 | ~50% | |
| NVads V710 v-5 | NVIDIA A100 | $3.00 | $1.50 | $1.00 | ~50% |
Key Takeaways:
- NG Family:
- On-demand pricing starts as low as $0.53/hour for the NGads V620 series, making it an affordable choice for graphics-heavy workloads.
- Significant savings are possible with reserved instances (up to 75%).
- Spot instances provide up to 81% savings, reducing costs to $0.10/hour.
- NV Family:
- While on-demand pricing starts higher (from $0.90/hour), the NV Family offers powerful GPUs for compute-intensive workloads.
- Reserved instances offer up to 50% savings, making them more affordable for longer-term use.
- Spot pricing provides substantial discounts, with savings of up to 67% for the most powerful instances (e.g., NVads V710 v-5).
Industry-Specific Applications
Each GPU family excels in different industries, with the NG family focusing on media and design, and the NV family dominating the AI and scientific research sectors.
- NG Family Use Cases
Entertainment & Rendering
NG GPUs, equipped with powerful graphics processing units, deliver fast, high-quality rendering for CGI, 3D models, and special effects. Their low-latency and multi-threaded capabilities are perfect for fast-paced production pipelines in gaming, animation, and film.
Key Hook: Up to 3x faster rendering compared to CPU-based rendering, streamlining production cycles for 3D animations and CGI effects in films and games.
Media & Broadcast
NG VMs excel in video transcoding with low-latency encoding and decoding. Ideal for media streaming platforms, NG VMs ensure smooth playback across multiple devices, reducing video buffering and streaming interruptions.
Key Hook: 2x faster video transcoding and reduced latency for real-time streaming, enabling seamless content delivery across diverse platforms and networks.
Virtual Workstations:
With virtual GPU capabilities, NG GPUs make remote access to high-end design software (such as Adobe Creative Suite, AutoCAD, etc.) seamless and responsive, allowing creative professionals to work remotely without sacrificing performance.
Key Hook: Up to 4x faster design rendering and flawless remote work for creative professionals, ensuring high productivity without requiring local hardware.
- NV Family Use Cases
AI & Deep Learning
NV GPUs are equipped with Tensor Cores, designed specifically to accelerate deep learning models. Their ability to handle large datasets with massive parallelism makes them ideal for training AI models, from neural networks to reinforcement learning.
Key Hook: Up to 10x faster AI training due to Tensor Cores, enabling quicker model iterations and faster results for deep learning workloads compared to CPU-based training.
Data Centers and HPC
NV GPUs handle complex simulations and large-scale data analysis, essential for industries like weather forecasting, biotech, and cryptography. The architecture is designed to scale efficiently across multiple nodes, ensuring fast computation and reliable performance.
Key Hook: Up to 5x faster simulations and better scalability for data-intensive tasks in high-performance computing environments like weather forecasting and molecular modeling.
High-Resolution Image Processing:
With large GPU memory and high throughput, NV GPUs are perfect for processing large datasets, such as satellite imagery, medical imaging, and high-resolution photos, where handling large files with complex calculations is crucial.
Key Hook: Up to 4x faster image processing for high-resolution datasets, ideal for industries like healthcare imaging and geospatial analysis where large files require fast, accurate processing.
Key Takeaways:
- NG Family: Best suited for real-time graphics and media-focused tasks like CGI rendering, video streaming, and virtual workstations for creative professionals.
- NV Family: Tailored for AI, machine learning, and compute-heavy workloads, excelling in scientific simulations, deep learning, and data processing.
After exploring the key use cases of Azure's NG and NV families, it’s clear that Azure GPUs are positioned at the cutting edge of cloud computing, providing tailored solutions to meet the demands of diverse industries.
Looking ahead, Azure’s NG family will continue to push the boundaries of media production, gaming, and interactive experiences, offering faster, high-quality graphics rendering and low-latency real-time video transcoding. On the other hand, Azure’s NV family is set to accelerate breakthroughs in AI, deep learning, and high-performance computing, with the latest advancements like the NVIDIA A100 and H100 GPUs driving faster model training and large-scale data processing.
The future of Azure GPUs is centered on delivering unmatched scalability, performance, and efficiency, empowering businesses to unlock new levels of innovation and performance in the cloud. Whether you’re advancing media technologies or developing next-gen AI applications, Azure’s GPU capabilities will continue to evolve to meet your needs.
