--- title: "NVIDIA B200 SXM | Aguasto" description: "Manufacturer reference for NVIDIA B200 SXM: Blackwell, individual accelerator, with sourced specifications, variant boundaries and deployment considerations." canonical: "https://www.aguasto.com/gpus/b200" last-updated: "2026-10-12" --- # NVIDIA B200 SXM | Aguasto Canonical HTML: [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) > Manufacturer reference for NVIDIA B200 SXM: Blackwell, individual accelerator, with sourced specifications, variant boundaries and deployment considerations. [Aguasto](https://www.aguasto.com/) [GPU Library](https://www.aguasto.com/gpus) NVIDIA B200 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 B200 SXM NVIDIA's HGX page states HGX B200 is shipping. This record describes one SXM GPU, not the baseboard. 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 Blackwell[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) · [NVIDIA HGX Platform (external source)](https://www.nvidia.com/en-us/data-center/hgx/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Category Individual accelerator[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) · [NVIDIA HGX Platform (external source)](https://www.nvidia.com/en-us/data-center/hgx/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Accelerator memory 180 GBPer B200 SXM accelerator in the reviewed enterprise reference configuration.[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 type HBM3e[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) · [NVIDIA HGX Platform (external source)](https://www.nvidia.com/en-us/data-center/hgx/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Memory bandwidth 8 TB/sUp to peak memory bandwidth per GPU.[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) Power 1,000 WConfigurable up to 1 kW per GPU; excludes CPU, networking and server losses.[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) Precision formats FP4, FP8, FP6, INT8, FP16, BF16, TF32, FP32, FP64[NVIDIA HGX Platform (external source)](https://www.nvidia.com/en-us/data-center/hgx/) Cooling / integration Verify the selected NVIDIA-certified OEM server cooling configuration; module specifications alone do not determine facility cooling requirements.[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) · [NVIDIA HGX Platform (external source)](https://www.nvidia.com/en-us/data-center/hgx/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) ### Configuration boundaries - Selected capacity is current B200 SXM, not a GB200 GPU or early architecture marketing capacity. - No normalized per-GPU throughput ranking is supplied from HGX aggregate figures. ### Deployment considerations - Use the actual allocatable memory of the chosen module and MIG profile rather than early architecture capacity announcements. - Do not divide baseboard throughput by GPU count and present the result as independently benchmarked GPU throughput. #### 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) [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) [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. - [HGX H100, H200 and B200 reference architecture components ↗](https://docs.nvidia.com/enterprise-reference-architectures/hgx-ai-factory-h100-h200-b200/latest/components.html) B200 SXM 180 GB HBM3e; Up to 8 TB/s GPU memory bandwidth; Configurable up to 1 kW per GPU. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [NVIDIA HGX Platform ↗](https://www.nvidia.com/en-us/data-center/hgx/) Blackwell architecture; HGX B200 shipping now; Published supported precision table; table totals belong to an eight-GPU baseboard. 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/b200#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.