Short answer: In NVIDIA’s HGX SXM reference configurations, H200 is a Hopper-generation step up from H100 in GPU memory capacity and bandwidth, while B200 moves to the Blackwell architecture and raises both again, alongside a newer, faster NVLink fabric. Those specifications help indicate how much data a system can hold and move, but they do not predict application speed or establish current stock. Availability depends on the seller or cloud provider; exports depend on the specific product and transaction.
H100 vs. H200 vs. B200: HGX SXM specifications
The figures below are NVIDIA platform specifications for its HGX SXM reference configurations, not guarantees for every card, server, or deployment. NVIDIA lists H100 and H200 as Hopper products and B200 as a Blackwell product.
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| HGX SXM configuration | Architecture | Memory per GPU | GPU memory bandwidth | Memory across eight GPUs | NVLink GPU-to-GPU bandwidth | Aggregate NVLink bandwidth |
|---|---|---|---|---|---|---|
| H100 | Hopper | 80 GB HBM3 | 3.35 TB/s | 640 GB | 900 GB/s | 7.2 TB/s |
| H200 | Hopper | 141 GB HBM3e | 4.8 TB/s | 1.1 TB (1,128 GB in NVIDIA’s reference architecture) | 900 GB/s | 7.2 TB/s |
| B200 | Blackwell | 180 GB HBM3e | Up to 8 TB/s | 1.44 TB | 1,800 GB/s | 14.4 TB/s |
Source: NVIDIA’s HGX H100/H200/B200 reference specifications and interconnect figures, accessed in 2026. The values describe the listed HGX configurations; specifications can differ in other product forms.
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Memory capacity affects model fit
GPU memory holds model weights, intermediate values, and other data needed during computation. More capacity can let a workload fit on fewer GPUs or support a larger model or batch, depending on its configuration. The eight-GPU totals in the table are combined capacity, not a single shared pool: software and workload design determine how data is distributed across GPUs.
#1 Best Overall
- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [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.
- [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.
- [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.
Bandwidth is not a speedup percentage
Memory bandwidth describes how quickly data can move between GPU memory and the processor under the stated specification. A higher figure can help memory-intensive workloads, but it does not mean every job will finish proportionally faster. Precision, software, power limits, model design, system configuration, and the workload itself all affect performance. The reference figures are not independent benchmark results.
Interconnect matters when GPUs work together
NVLink and NVSwitch connect GPUs within an HGX system. NVIDIA specifies the same NVLink generation and bandwidth for H100 and H200 HGX systems, while B200’s reference configuration uses the next listed generations and higher bandwidth. That can matter for multi-GPU work that exchanges data frequently, but overall results also depend on server design, networking beyond the GPU fabric, and software.
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.
Which configuration should you evaluate?
Start with the workload and system rather than treating the newest GPU as an automatic winner. Use the comparison as a shortlist, then ask vendors for evidence that matches your deployment.
- Model fit: Check whether the complete workload fits in memory at the intended precision, batch size, and number of GPUs.
- Measured performance: Request benchmarks for the same model, precision, software stack, and system configuration you plan to use. A vendor specification alone cannot establish an application-level speedup.
- Whole-system requirements: Compare server cost, power, cooling, and network design alongside accelerator specifications. No current price comparison is established here.
- Supply and deployment: Confirm a current delivery commitment or cloud capacity directly with the OEM, channel partner, or cloud provider.
- Destination and compliance: Check licensing and export requirements for the exact product, parties, end use, and route before ordering or shipping.
Availability: historical announcements are not live inventory
NVIDIA announced H200 in November 2023 and said systems were expected to be available from global system manufacturers and cloud providers starting in Q2 2024. That announcement is a historical target, not evidence that a particular system is in stock now.
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
NVIDIA later identified CoreWeave as the first cloud provider to announce general availability of H200-powered systems. This records a company-reported deployment milestone; it does not establish current capacity, pricing, or availability from CoreWeave or any other provider.
For a purchase or deployment, check a current OEM, channel-partner, or cloud-provider listing and confirm the configuration, region, capacity, and delivery timing directly. NVIDIA says fulfillment hardware support for its architecture is provided through OEMs and channel partners; its architecture information also describes NVIDIA AI Enterprise software support as a paid per-GPU subscription. Those support arrangements do not establish hardware stock or a quoted system price.
Rank #4
- Discrete graphics card memory 40 GB
- Memory bandwidth (max) 1555 GB/s
- Graphics processor family NVIDIA
- Graphics processor A100
Export restrictions: what the cited U.S. policy says
H200 shipments to China are not blanket-authorized
On January 13, 2026, the U.S. Bureau of Industry and Security (BIS) said it would review license applications for H200, AMD MI325X, and similar chips for export to China case by case, subject to security requirements. BIS identified conditions including preserving semiconductor production capacity available to U.S. customers, export-compliance procedures and customer screening by the Chinese purchaser, and independent third-party performance and security testing in the United States. A case-by-case review policy is not blanket permission to export every H200 to every China-based buyer.
H100 and B200 also require destination-specific review
NVIDIA’s August 2026 Form 10-Q describes U.S. licensing controls for products above specified performance thresholds and names H100 and B200 among examples affected by controls for China and certain other destinations. The same filing says the U.S. government began granting licenses in February 2026 for small amounts of H200 products to specific China-based customers, while PRC government restrictions prevented NVIDIA from selling all products for which it had licenses. These are company disclosures about licensing and commercial impact, not a transaction-specific determination.
Check the complete transaction, not just the chip name
Whether a shipment can proceed depends on the exact product and configuration, destination, consignee and ownership, end use, and routing. Classification, applicable Export Administration Regulations, license exceptions, and reexport rules may also matter. Consult current BIS requirements and qualified export counsel for a live transaction; a general article cannot determine whether a particular shipment is authorized.
Sources and scope
The technical comparison reflects NVIDIA’s HGX reference specifications, not independent testing. Availability statements are dated NVIDIA announcements rather than live seller listings. The export discussion reflects BIS’s January 2026 policy announcement and NVIDIA’s August 2026 Form 10-Q, and does not replace checking current regulations or obtaining legal advice. Availability and export rules can change after those publication dates.
Quick Recap
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