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There is no reliable, source-supported single-number answer to whether the H100 or H20 is faster. NVIDIA publishes detailed specifications for H100 SXM and H100 NVL, while the available NVIDIA H20 documentation confirms 96GB and 141GB SXM5 variants but does not provide a comparable current performance specification. For an AI deployment, compare the exact GPU and server configuration against model memory needs, throughput targets, multi-GPU scaling, power and cooling, and whether the hardware can be procured for your destination and organization.
NVIDIA H100 vs. H20: what the documented specifications show
“H100” is not one uniform configuration. NVIDIA’s product page lists SXM and NVL versions with different memory capacities, bandwidth, interconnect and power specifications. The H20 details available in NVIDIA’s AI Enterprise vGPU documentation identify SXM5 memory variants and vGPU profiles, rather than a full, directly comparable hardware specification table.
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| Specification | H100 SXM | H100 NVL | H20 SXM5 |
|---|---|---|---|
| Documented memory | 80GB | 94GB | 96GB and 141GB variants |
| Memory bandwidth | 3.35TB/s | 3.9TB/s | Not stated in the cited NVIDIA vGPU documentation |
| FP8 Tensor Core rate | 3,958 teraFLOPS, with sparsity | 3,341 teraFLOPS, with sparsity | Not stated in the cited NVIDIA vGPU documentation |
| NVLink | 900GB/s | 600GB/s | Not stated in the cited NVIDIA vGPU documentation |
| Configurable power | Up to 700W | 350–400W | Not stated in the cited NVIDIA vGPU documentation |
H100 figures are NVIDIA product specifications, not independent benchmark results; the Tensor Core rates are marked as using sparsity on NVIDIA’s H100 specifications page. H20 memory variants and vGPU profiles are documented in NVIDIA AI Enterprise’s Hopper vGPU reference. The “not stated” entries mean those cited documents do not establish a matching H20 figure for this comparison; they are not estimates of H20 capability.
How to choose for an AI workload
Start with model and workload memory
Check whether the model weights, runtime overhead, context or sequence length, and intended batch size fit the usable memory of the exact GPU configuration. Memory capacity can determine whether a workload fits on one GPU or requires sharding or additional devices. The documented H20 memory options exceed the listed H100 SXM capacity, but capacity alone does not establish greater throughput or overall suitability. Compare the exact variant and system rather than treating either product name as a single memory configuration.
#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.
Match compute and throughput needs to evidence
If your decision depends on tokens per second, training time, latency, or throughput at a particular precision, require results for the same model, software stack, precision, batch or concurrency, and system configuration. The available official H100 figures do not provide a comparable H20 compute-rate table, so they cannot support an exact H100-to-H20 compute ratio. Do not treat a rate from one H100 form factor as a direct H20 benchmark.
NVIDIA describes the H100 Transformer Engine’s FP8 feature as providing “up to 4X faster training” for GPT-3 (175B) compared with the prior generation. That is NVIDIA’s stated prior-generation comparison, not an H100-versus-H20 result.
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.
Check multi-GPU scaling and the complete server
The listed H100 NVLink figures differ by form factor: 900GB/s for SXM and 600GB/s for NVL. The cited H20 documentation does not establish a matching interconnect figure. For distributed training or inference, ask the system vendor for the exact baseboard, GPU-to-GPU topology, supported GPU count, networking, and workload-specific scaling data. A GPU specification by itself does not describe a complete multi-GPU system.
Include power, cooling and deployment constraints
NVIDIA lists H100 SXM at up to 700W configurable and H100 NVL at 350–400W configurable. The cited H20 material does not provide a comparable power figure. Have the supplier validate rack power, cooling, system limits and sustained operating conditions for the proposed server; do not infer H20 system requirements from an H100 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
H20 procurement depends on destination and buyer
H20 availability is not just a stock question. NVIDIA’s fiscal 2027 second-quarter Form 10-Q, published August 27, 2026, says the U.S. government informed the company in April 2025 that a license was required for H20 exports to China, including Hong Kong and Macau, and D:5 countries, or to companies headquartered there or with an ultimate parent there. NVIDIA also reported that licenses granted beginning in August 2025 allowed certain shipments, while PRC government restrictions limited sales. These are dated company disclosures; rules, license conditions and commercial availability can change, and they do not determine the eligibility of every buyer. Review the current rules and obtain supplier confirmation for your specific destination, organization and transaction. See NVIDIA’s Form 10-Q.
Quick Recap
Rank #4
- Discrete graphics card memory 40 GB
- Memory bandwidth (max) 1555 GB/s
- Graphics processor family NVIDIA
- Graphics processor A100
A practical decision checklist
- Identify the exact SKU and system: distinguish H100 SXM from H100 NVL, and verify the H20 memory variant and server configuration.
- Confirm the workload fit: establish required memory, precision, latency and throughput using the model and deployment conditions you actually intend to run.
- Request comparable evidence: ask for same-workload benchmarks and details of software, concurrency, batch size, power limits and multi-GPU topology.
- Validate infrastructure: verify power, cooling, interconnect and networking for the complete system.
- Clear procurement before planning around H20: confirm regional rules, licensing applicability and supplier eligibility for the destination and customer.
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