--- title: "NVIDIA H100 SXM | Aguasto" description: "Manufacturer reference for NVIDIA H100 SXM: Hopper, individual accelerator, with sourced specifications, variant boundaries and deployment considerations." canonical: "https://www.aguasto.com/gpus/h100" last-updated: "2026-10-12" --- # NVIDIA H100 SXM | Aguasto Canonical HTML: [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) > Manufacturer reference for NVIDIA H100 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 H100 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 H100 SXM A documented commercial SXM accelerator; this record does not establish supplier stock or an Aguasto allocation. 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 H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) · [HGX H100, H200 and B200 reference architecture components (external source)](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Category Individual accelerator[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) · [HGX H100, H200 and B200 reference architecture components (external source)](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Accelerator memory 80 GBPer H100 SXM accelerator, excluding host memory; not H100 NVL.[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) Memory type HBM3[HGX H100, H200 and B200 reference architecture components (external source)](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) Memory bandwidth 3.35 TB/sPeak GPU memory bandwidth per SXM accelerator, not NVLink bandwidth.[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) Power 700 WMaximum configurable GPU TDP, not server input or facility power.[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) Precision formats FP64, FP32, TF32, BF16, FP16, FP8, INT8[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) Cooling / integration SXM server integration; verify the OEM server cooling design. H100 NVL PCIe air-cooling specifications must not be applied to this SXM record.[NVIDIA H100 GPU specifications (external source)](https://www.nvidia.com/en-us/data-center/h100/) · [HGX H100, H200 and B200 reference architecture components (external source)](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) ### Configuration boundaries - The headline fields select the SXM variant. H100 NVL has a different capacity, power range and form factor. - Manufacturer peak Tensor Core throughput values may include structured sparsity; do not treat them as measured tokens per second. ### 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 H100 SXM; manufacturer peak Tensor Core specification with sparsity[Manufacturer source ↗](https://www.nvidia.com/en-us/data-center/h100/) ### Deployment considerations - Reserve accelerator memory for KV cache, activations and serving overhead in addition to model weights. - Check server topology and collective communication behavior before choosing tensor or pipeline parallelism. #### 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 H100 GPU specifications ↗](https://www.nvidia.com/en-us/data-center/h100/) Hopper architecture; SXM memory 80 GB; SXM bandwidth 3.35 TB/s; SXM maximum configurable TDP 700 W; precision support; SXM versus NVL variant distinction. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [HGX H100, H200 and B200 reference architecture components ↗](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) H100 SXM HBM3 memory type. 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/h100#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. 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