High-performance graphics processors have evolved considerably in recent years. As GPU servers become increasingly important for compute-intensive applications, choosing hardware that matches your specific requirements is more important than ever.

Key Takeaways

The key facts about server GPUs at a glance:

  • Eight server GPUs and AI accelerators are compared: NVIDIA H200, H100, RTX PRO 6000, L40S, A30 and B200, Intel Gaudi 3 and AMD Instinct MI350X.
  • For each model you will find the technical details on architecture, memory, memory bandwidth, compute performance, interconnect and power consumption, along with its advantages and disadvantages.
  • For AI training and deep learning, memory bandwidth, compute performance and scalability are decisive.
  • For AI inference, efficiency, memory capacity and the specific workload matter most.
  • In high performance computing, high compute performance and memory bandwidth are the key factors.
  • For big data and analytics, high memory throughput is crucial.
  • For edge computing and smaller clusters, what matters is low power consumption and compact infrastructure.

How do server GPUs compare in terms of performance?

NVIDIA H200

The NVIDIA H200 is particularly well suited to workloads that process large volumes of data directly in GPU memory. Typical use cases include AI inference with large language models, retrieval-based AI applications, scientific simulations and other memory-intensive HPC workloads. Its main strengths are its large memory capacity, high bandwidth and powerful computing performance for demanding applications.

Technical details

  • Architecture: NVIDIA Hopper
  • Memory: 141 GB HBM3e
  • Memory bandwidth: 4.8 TB/s
  • Compute performance: Up to 34 TFLOPS (FP64), up to 67 TFLOPS (FP64 Tensor Core), up to 3,958 TFLOPS (FP8, with sparsity)
  • Interconnect/scaling: NVLink with up to 900 GB/s, MIG support
  • Power consumption: Depending on the variant, up to 700 watts

Advantages and disadvantages

Advantages Disadvantages
Very large and fast memory for demanding AI workloads High acquisition cost
High compute performance for inference, training and HPC High power consumption
Good scalability Often oversized for smaller standard workloads

NVIDIA H100

The NVIDIA H100 is a high-performance data centre GPU designed for compute-intensive AI and HPC workloads. Typical use cases include training large AI models, complex data analysis, simulations and scalable cluster environments in research and enterprise IT. Its main strengths are its high computing performance and broad compatibility with established AI and HPC software stacks.

Technical details

  • Architecture: NVIDIA Hopper
  • Memory: 80 GB or 94 GB HBM3
  • Memory bandwidth: 3.35 to 3.9 TB/s
  • Compute performance: Up to 34 TFLOPS (FP64), up to 67 TFLOPS (FP64 Tensor Core), up to 3,958 TFLOPS (FP8, with sparsity)
  • Interconnect/scaling: NVLink with up to 900 GB/s, MIG support
  • Power consumption: Up to 700 watts, depending on the variant

Advantages and disadvantages

Advantages Disadvantages
Excellent performance for AI training and inference Very high acquisition cost
Supports cutting-edge memory technologies High energy consumption (TDP up to 700 watts)
Scalability through NVLink

NVIDIA RTX PRO 6000

The NVIDIA RTX PRO 6000 Blackwell Server Edition is designed for users who need a server GPU that can handle a broad range of professional workloads. Alongside AI applications such as inference, fine-tuning and multimodal models, it is suitable for rendering, 3D visualisation, CAD, video processing and virtual workstations. This makes it especially useful in environments where AI and graphics workloads share the same infrastructure.

Technical details

  • Architecture: NVIDIA Blackwell
  • Memory: 96 GB GDDR7 with ECC
  • Memory bandwidth: 1,597 GB/s
  • Compute performance: Up to 120 TFLOPS (FP32), up to 2 PFLOPS (FP8, with sparsity), up to 4 PFLOPS (FP4, with sparsity)
  • Interconnect/scaling: PCIe Gen 5, multi-GPU operation in server environments
  • Power consumption: Up to 600 watts

Advantages and disadvantages

Advantages Disadvantages
Very versatile for AI, graphics and media workloads High power consumption
Large GDDR7 memory for complex projects High price in the enterprise segment

NVIDIA L40S

The NVIDIA L40S is designed for companies that need a versatile GPU for production-grade AI and visual computing scenarios. It shows its strengths particularly in AI inference, image and video editing, 3D rendering, digital twins and virtual desktop environments. This makes it especially suitable for environments where the focus is not on a single specialised workload, but on a broad spectrum of professional applications.

Technical details

  • Architecture: NVIDIA Ada Lovelace
  • Memory: 48 GB GDDR6 with ECC
  • Memory bandwidth: 864 GB/s
  • Compute performance: Up to 91.6 TFLOPS (FP32), up to 1,466 TFLOPS (FP8, with sparsity)
  • Interconnect/scaling: PCIe Gen 4, vGPU support
  • Power consumption: 350 watts

Advantages and disadvantages

Advantages Disadvantages
Wide range of use cases for AI, graphics and video Lower memory capacity for very large models
Well suited for inference, rendering and virtual workstations Less geared towards extreme scaling
Suitable for enterprise use thanks to vGPU support Focuses more on versatility than on specialised peak workloads

NVIDIA A30

The NVIDIA A30 is a solid server GPU for classic tasks where efficiency and a balanced price-performance ratio are more important than maximum peak performance. It is particularly suitable for AI inference, smaller training jobs, data analysis, virtualisation and moderately demanding HPC applications. For areas of use with established standard workloads, it is therefore a practical solution when no high-end GPU is required.

Technical details

  • Architecture: NVIDIA Ampere
  • Memory: 24 GB HBM2
  • Memory bandwidth: 933 GB/s
  • Compute performance: 5.2 TFLOPS (FP64), 10.3 TFLOPS (FP64 Tensor Core), up to 165 TFLOPS (BF16/FP16, dense)
  • Interconnect/scaling: NVLink bridge for up to two GPUs, MIG support
  • Power consumption: 165 watts

Advantages and disadvantages

Advantages Disadvantages
Solid price-performance ratio for mainstream workloads Limited memory for very large models
Comparatively low power consumption Only partially suitable for highly scaled AI scenarios
Fewer reserves for particularly compute-intensive applications

Intel Gaudi 3

Intel Gaudi 3 is a server accelerator designed specifically for AI workloads, primarily aimed at users who want to train or run inference on large language models, generative AI and other compute-intensive deep learning applications. The focus is on high memory bandwidth, a large memory configuration and a network architecture designed for scaling, based on standard Ethernet. This makes Gaudi 3 particularly suitable for AI clusters where training and inference workloads are to be efficiently distributed across multiple accelerators.

Technical details

  • Architecture: Intel Gaudi 3 AI Accelerator, manufactured using TSMC’s 5 nm process.
  • Memory: 128 GB HBM2e.
  • Memory bandwidth: 3.7 TB/s.
  • Compute performance: Up to 1.8 PFLOPS at FP8 and BF16 (dense).
  • Interconnect/scaling: 24 integrated 200 GbE RDMA ports with RoCE v2, PCIe Gen5 x16.
  • Power consumption: Up to 900 watts.

Advantages and disadvantages

Advantages Disadvantages
Highly suitable for training and inference of large AI models Strong focus on AI workloads
Large memory configuration and high memory bandwidth High power consumption
Scaling via standard Ethernet and open software tools

AMD Instinct MI350X

The AMD Instinct MI350X is designed for data centre workloads in the areas of generative AI, training, inference and high-performance computing. It is primarily aimed at companies and research institutions that want to process large models, run data-intensive simulations or build scalable AI infrastructures. Its strength lies in the combination of very large HBM3E memory, high memory bandwidth and an open software environment based on ROCm.

Technical details

  • Architecture: AMD CDNA 4.
  • Memory: 288 GB HBM3E.
  • Memory bandwidth: 8 TB/s.
  • Compute performance: Up to 72.1 TFLOPS (FP64), 144.2 TFLOPS (FP32), 4.614 PFLOPS (FP16/BF16, with sparsity) and 9.227 PFLOPS (MXFP4/MXFP6, dense).
  • Interconnect/scaling: 7 AMD Infinity Fabric Links with 153 GB/s each, plus PCIe 5.0 x16.
  • Power consumption: Up to 1,000 watts.

Advantages and disadvantages

Advantages Disadvantages
Very large memory configuration for extensive AI and HPC workloads High power consumption
High memory bandwidth for data-intensive applications More suitable for specialised data centre environments
Open software stack with ROCm support

NVIDIA B200

The NVIDIA B200 is a Blackwell-based high-performance accelerator for data centres, developed primarily for large-scale AI training and inference workloads. It is typically used in HGX and DGX platforms, where multiple GPUs are closely interconnected via NVLink and NVSwitch. This makes the B200 particularly suitable for AI factory scenarios, very large language models, data-intensive analytics and HPC applications with high scaling requirements.

Technical details

  • Architecture: NVIDIA Blackwell.
  • Memory: 180 GB HBM3E per GPU.
  • Memory bandwidth: Up to 8 TB/s per GPU.
  • Compute performance: In 8-GPU HGX-B200 systems, up to 144 PFLOPS (FP4, with sparsity), 72 PFLOPS (FP8/FP6, with sparsity) and 36 PFLOPS (FP16/BF16, with sparsity).
  • Interconnect/scaling: NVLink and NVSwitch; in 8-GPU systems, up to 14.4 TB/s aggregate NVLink bandwidth.
  • Power consumption: Configurable up to 1,000 watts per GPU.

Advantages and disadvantages

Advantages Disadvantages
Very high AI compute performance for large training and inference workloads Very high energy and cooling requirements
Large HBM3E memory for memory-intensive models Mainly suitable for large data centre environments
Very good scalability via NVLink and NVSwitch

Which use cases are server GPUs best suited to?

Which server GPU is suitable for your company depends entirely on your specific use case. Before making an investment, you should therefore analyse your workloads and assess the long-term requirements of your applications.

AI training and deep learning

For training large neural networks, particularly transformer models such as GPT, memory bandwidth, computing performance and scalability are key. In data centres, the NVIDIA H200 and NVIDIA B200 are well suited to very large AI training workloads. Intel Gaudi 3 and AMD Instinct MI350X are also designed for training and inference with large models, making them relevant for suitably configured AI and HPC infrastructures. The NVIDIA H100 remains a powerful option for existing AI environments and established cluster setups.

Recommendation:

  • High-end: NVIDIA B200
  • For memory-intensive AI workloads: NVIDIA H200

AI inference

For inference – using trained models to generate results – efficiency, memory capacity and the type of workload are key considerations. The NVIDIA RTX PRO 6000 Blackwell Server Edition is well suited to modern AI applications with varied requirements, including multimodal workloads, visual computing and production-focused enterprise use cases. The NVIDIA L40S is another strong option for mixed inference, graphics and media workloads. For highly scalable inference environments, the NVIDIA B200 may be a suitable choice, while the AMD Instinct MI350X is designed for memory-intensive AI workloads in appropriately configured data centres. In more cost-conscious environments, the NVIDIA A30 can still offer a solid alternative.

Recommendation:

  • High-end: NVIDIA RTX PRO 6000
  • Budget option: NVIDIA A30

High performance computing

For scientific calculations and simulations in the area of High Performance Computing, high compute performance and memory bandwidth are particularly important. The NVIDIA H200 is a particularly strong choice here. The NVIDIA B200 and AMD Instinct MI350X are also interesting for demanding high performance computing workloads, especially in data centre environments with correspondingly high scalability. The NVIDIA H100 also remains relevant for many HPC scenarios, particularly in existing infrastructures with established software stacks. For smaller simulations or less demanding workloads, the NVIDIA A30 can still be sufficient.

Recommendation:

  • High-end: NVIDIA RTX PRO 6000
  • Budget option: NVIDIA A30

Big data and analytics

For data-intensive applications such as real-time analytics, high memory throughput is crucial. The NVIDIA H200 is particularly impressive here, as it is designed for memory-intensive AI and HPC workloads. The NVIDIA B200 and AMD Instinct MI350X are also interesting for large data volumes and compute-intensive analytics scenarios, especially when these are closely linked with AI or HPC workloads. Intel Gaudi 3 also remains an option for large AI-adjacent data processing tasks.

Recommendation:

  • NVIDIA H200
  • NVIDIA B200
  • AMD Instinct MI350X
  • Intel Gaudi 3

Edge computing and smaller clusters

For applications such as edge computing, where lower power consumption and compact infrastructure are important, the NVIDIA A30 remains a suitable option. It is particularly well suited to inference, analytics and virtualisation in smaller environments. The NVIDIA L40S can also be a good choice for versatile AI and visual computing workloads, especially when graphics or rendering tasks are involved.

Recommendation:

  • NVIDIA A30
  • NVIDIA L40S
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