--- title: "NVIDIA H200 SXM | Aguasto" description: "Manufacturer reference for NVIDIA H200 SXM: Hopper, individual accelerator, with sourced specifications, variant boundaries and deployment considerations." canonical: "https://www.aguasto.com/gpus/h200" last-updated: "2026-10-12" --- # NVIDIA H200 SXM | Aguasto Canonical HTML: [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) > Manufacturer reference for NVIDIA H200 SXM: Hopper, individual accelerator, with sourced specifications, variant boundaries and deployment considerations. [Aguasto](https://www.aguasto.com/) [GPU Library](https://www.aguasto.com/gpus) NVIDIA H200 SXM [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) NVIDIA / Individual accelerator ## NVIDIA H200 SXM The manufacturer product page explicitly says available. No supplier stock or Aguasto deployment is verified. Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### Manufacturer specification reference Architecture Hopper[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Category Individual accelerator[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Accelerator memory 141 GBPer H200 SXM GPU; host memory excluded.[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) Memory type HBM3e[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Memory bandwidth 4.8 TB/sPeak GPU memory bandwidth per accelerator.[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) Power 700 WMaximum configurable SXM GPU TDP; not H200 NVL or complete server draw.[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) Precision formats FP64, FP32, TF32, BF16, FP16, FP8, INT8[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) Cooling / integration SXM server thermal implementation depends on OEM system. The separately documented H200 NVL is a dual-slot air-cooled PCIe product.[NVIDIA H200 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h200/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) ### Configuration boundaries - NVIDIA retains a preliminary/specifications-may-change footnote on this page; preserve that qualification. - Headline power selects SXM. NVL has a different published power limit. ### Published arithmetic throughput Manufacturer peaks, not Aguasto measurements or tokens per second **Specification** FP8 peak compute **Precision** FP8 **Published peak** 3,958 TFLOPS **Boundary & source** One H200 SXM; manufacturer peak Tensor Core specification with sparsity[Manufacturer source ↗](https://www.nvidia.com/en-us/data-center/h200/) ### Deployment considerations - Larger GPU memory can accommodate more weights or cache, but capacity alone does not establish latency or concurrency. - Confirm the serving framework, GPU driver and selected model format together. #### Software ecosystem CUDA. Confirm the exact model, runtime release, server integration and vendor support matrix before evaluating a deployment. ### Continue the research [vLLM](https://www.aguasto.com/inference-stack/vllm) [SGLang](https://www.aguasto.com/inference-stack/sglang) [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) [Model weights versus runtime memory](https://www.aguasto.com/learn/model-weights-and-runtime-memory) [Compare compatible hardware categories](https://www.aguasto.com/gpus/compare) [Why modular infrastructure?](https://www.aguasto.com/why-modular) ### Sources & verification Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [NVIDIA H200 GPU specifications ↗](https://www.nvidia.com/en-us/data-center/h200/) Explicit availability statement; Hopper architecture; 141 GB HBM3e; 4.8 TB/s memory bandwidth; SXM maximum configurable TDP 700 W; precisions; SXM and NVL distinctions. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [CUDA GPU Compute Capability ↗](https://developer.nvidia.com/cuda/gpus) CUDA product identification for H100, H200, B200, B300, GB200 and GB300. Publication: date not stated in the reviewed source. Verified: 2026-10-12. ### Reference journey [Compare hardware ↗](https://www.aguasto.com/gpus/compare) [Model Index ↗](https://www.aguasto.com/models) [Serving software ↗](https://www.aguasto.com/inference-stack) [OEM collaboration ↗](https://www.aguasto.com/partners) [Sources & verification ↓](https://www.aguasto.com/gpus/h200#sources) Research reference · No available Aguasto service ## Company navigation - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/why-modular) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.