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Short answer: Choose H100 when its memory and performance meet your workload and a supported system fits your budget; consider H200 when memory capacity or bandwidth is a constraint; and evaluate B200 for workloads that can use its higher published memory bandwidth and a Blackwell-based HGX system. None is a universal winner: compare validated results on your actual model, software stack, and complete server configuration.
The figures below compare NVIDIA’s SXM GPUs in HGX reference architectures, not every product variant. H100 NVL, for example, has different memory and deployment characteristics from H100 SXM. System design, power and cooling, GPU interconnect, and current system cost also affect the choice.
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How do H100, H200, and B200 compare?
NVIDIA’s HGX reference architecture lists the following specifications for these SXM GPU configurations. Memory bandwidth and capacity are GPU specifications; aggregate memory figures describe the eight-GPU HGX systems NVIDIA lists, not one GPU.
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|---|---|---|---|---|
| H100 SXM | Hopper, HBM3 | 80GB | 3.35TB/s | 640GB |
| H200 SXM | Hopper, HBM3e | 141GB | 4.8TB/s | About 1.1TB |
| B200 SXM | Blackwell, HBM3e | 180GB | Up to 8TB/s | Up to 1.44TB |
These are NVIDIA-published HGX reference-architecture specifications, accessed October 4, 2026; they are not independent benchmark results. The same reference architecture describes HGX systems with eight GPUs, NVLink/NVSwitch, host CPUs, memory, networking, and storage. See NVIDIA’s HGX reference architecture.
#1 Best Overall
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- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
- [DisplayPort 2.1] Achieve unparalleled visual clarity and performance, driving high resolution displays at up to 8K at 240 Hz and 16K at 60 Hz. Increased bandwidth enables seamless multi-monitor setups while HDR and higher color depth support ensures superior color accuracy for precision work, such as video editing, 3D design, and live broadcasting.
- [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations. | [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
Which GPU fits each workload?
Large-model inference
H200 is worth evaluating when a model, context length, or serving target is constrained by GPU memory capacity or bandwidth. Its 141GB of HBM3e and 4.8TB/s published bandwidth in the HGX SXM comparison exceed H100 SXM’s 80GB and 3.35TB/s. More memory can affect whether a model and its working data fit on a GPU or how a serving configuration is designed, but it does not by itself establish a particular latency or throughput gain.
NVIDIA’s H200 product page presents results of 1.9X faster for Llama 2 70B inference and 1.6X faster for GPT-3 175B inference, alongside workload conditions and GPU and batch details. Treat these as NVIDIA’s workload-specific comparisons, not a forecast for every model or serving stack. Check the conditions and methodology on NVIDIA’s H200 product page, which labels specifications preliminary and subject to change.
Rank #2
- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
- Second-Generation RT Cores - With up to 2X the throughput over the previous generation and the ability to concurrently run ray tracing with either shading or denoising capabilities, second-generation RT Cores deliver massive speedups for workloads like photorealistic rendering of movie content, architectural design evaluations, and virtual prototyping of product designs. This technology also speeds up the rendering of ray-traced motion blur for faster results with greater visual accuracy.
- Third-Generation Tensor Cores - New Tensor Float 32 (TF32) precision provides up to 5X the training throughput over the previous generation to accelerate AI and data science model training without requiring any code changes. Hardware support for structural sparsity doubles the throughput for inferencing. Tensor Cores also bring AI to graphics with capabilities like DLSS, AI denoising, and enhanced editing for select applications.
- Third-Generation NVIDIA NVLink - Increased GPU-to-GPU interconnect bandwidth provides a single scalable memory to accelerate graphics and compute workloads and tackle larger datasets.
- 48 Gigabytes (GB) of GPU Memory - Ultra-fast GDDR6 memory, scalable up to 96 GB with NVLink, gives data scientists, engineers, and creative professionals the large memory necessary to work with massive datasets and workloads like data science and simulation.
Training and multi-GPU workloads
For training, compare the full node and cluster rather than GPU specifications in isolation. GPU count and GPU-to-GPU fabric affect scaling; host CPU and system memory, network, storage throughput, software, and facility power and cooling can also constrain a run. NVIDIA describes HGX reference systems for multi-node AI and hybrid workloads in its HGX reference architecture.
H100 may remain suitable if your model, precision, and scaling plan fit its memory and your tested system meets the target. H200 or B200 may be better candidates when their memory or bandwidth is useful to the workload, but the architecture specifications alone do not prove a training-speed advantage. Validate with the intended GPU count, interconnect, framework, and configuration.
Rank #3
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
HPC
NVIDIA positions H200 and HGX systems for high-performance computing as well as AI. For HPC, compare application-specific results at the required precision and scale, and check whether the working set fits the available memory. The cited product and reference pages do not establish a universal HPC winner among these GPUs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the exact GPU variant and server matter
H100 and H200 variants are not interchangeable
The HGX table is specifically an SXM comparison. NVIDIA separately lists H100 SXM with 80GB and H100 NVL with 94GB; NVL differs in power, form factor, interconnect, and deployment options. H200 is listed with 141GB for both SXM and NVL, but those variants still differ in power, form factor, and system options. Confirm the exact SKU and supported server configuration in NVIDIA’s H100 specifications and NVIDIA’s H200 specifications.
Rank #4
- Discrete graphics card memory 40 GB
- Memory bandwidth (max) 1555 GB/s
- Graphics processor family NVIDIA
- Graphics processor A100
Evaluate the complete system
A GPU’s published specifications do not tell you whether a particular server can support the configuration your workload needs. Verify the supported baseboard and server, GPU interconnect, host components, network and storage, and the system’s power and cooling envelope. NVIDIA’s HGX documentation describes platform configurations; a GPU comparison should not be treated as a substitute for validating the whole node and cluster.
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- Define the workload. Record the model or application, precision, memory footprint, target latency or throughput, and whether the work runs on one GPU, one node, or a cluster.
- Check memory fit first. Compare the actual working set with the exact GPU variant’s capacity, allowing for the software and workload overhead relevant to your setup.
- Benchmark the intended configuration. Use the same model, input and output lengths, batch size, GPU count, software stack, and system configuration you plan to deploy. Measure the metric that matters to you, such as throughput, latency, or time to solution.
- Validate the platform. Confirm the server supports the selected SKU and GPU count, and that interconnect, host resources, networking, storage, power, and cooling match the deployment.
- Compare total system cost and procurement facts. Get current quotes and availability from vendors or sellers for the compatible system, not just a nominal GPU SKU. The cited NVIDIA pages do not establish current market prices, lead times, or regional availability.
What NVIDIA’s B200-versus-H100 claim does—and does not—say
NVIDIA’s HGX reference architecture claims the B200 baseboard delivers 15 times the performance and 12 times the TCO of the H100 baseboard for x86 scale-up platforms and infrastructure. This is a vendor claim with that stated platform scope, not an independent result or a guarantee for every workload. It should not replace a comparison using your application, deployment, and cost assumptions.
Quick Recap
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