--- title: "NVIDIA GB200 NVL72 | Aguasto" description: "Manufacturer reference for NVIDIA GB200 NVL72: Grace Blackwell, rack system, with sourced specifications, variant boundaries and deployment considerations." canonical: "https://www.aguasto.com/gpus/gb200-nvl72" last-updated: "2026-10-12" --- # NVIDIA GB200 NVL72 | Aguasto Canonical HTML: [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) > Manufacturer reference for NVIDIA GB200 NVL72: Grace Blackwell, rack system, with sourced specifications, variant boundaries and deployment considerations. [Aguasto](https://www.aguasto.com/) [GPU Library](https://www.aguasto.com/gpus) NVIDIA GB200 NVL72 [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 / Rack-scale system ## NVIDIA GB200 NVL72 NVIDIA reports GB200 NVL72 shipments began in 2024. Availability of a specific OEM build remains supplier-specific. 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 Grace Blackwell[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) · [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer (external source)](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Category Complete rack-scale reference[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) · [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer (external source)](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Accelerator memory 13,400 GBPublished rounded 13.4 TB aggregate GPU HBM converted to decimal GB; CPU memory excluded.[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) Memory type HBM3e[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) · [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer (external source)](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Memory bandwidth 576 TB/sPublished aggregate GPU memory bandwidth across the rack, not bandwidth available to one GPU or NVLink bandwidth.[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) Power Not verified for this recordTotal operating rack draw not verified in the reviewed product source. No power estimate is derived from GPU count.[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) · [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer (external source)](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) Precision formats NVFP4, FP8, FP6, INT8, FP16, BF16, TF32, FP32, FP64[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) Cooling / integration Liquid-cooled rack architecture.[NVIDIA GB200 NVL72 (external source)](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) · [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer (external source)](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) · [CUDA GPU Compute Capability (external source)](https://developer.nvidia.com/cuda/gpus) ### Configuration boundaries - A rounded decimal-TB rack total is preserved; it is not an exact allocatable memory guarantee. - Manufacturer throughput is a theoretical rack figure and does not predict delivered tokens per second. ### Published arithmetic throughput Manufacturer peaks, not Aguasto measurements or tokens per second **Specification** NVFP4 peak compute **Precision** NVFP4 **Published peak** 720 PFLOPS **Boundary & source** Whole rack, manufacturer peak dense specification[Manufacturer source ↗](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) **Specification** NVFP4 peak compute **Precision** NVFP4 **Published peak** 1,440 PFLOPS **Boundary & source** Whole rack, manufacturer peak sparse specification[Manufacturer source ↗](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) ### Deployment considerations - Treat rack HBM as distributed memory connected through a scale-up fabric, not one locally attached allocation. - Validate liquid-cooling interfaces, facility heat rejection, electrical redundancy and OEM acceptance tests. #### Software ecosystem CUDA · NVIDIA Mission Control. Confirm the exact model, runtime release, server integration and vendor support matrix before evaluating a deployment. ### Continue the research [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) [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 GB200 NVL72 ↗](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) 72 Blackwell GPUs and 36 Grace CPUs; 13.4 TB aggregate HBM3e; 576 TB/s aggregate GPU memory bandwidth; Liquid cooling; Precision table; Mission Control. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer ↗](https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/) GB200 NVL72 first shipments in 2024; GB300 NVL72 shipments in 2025. 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/gb200-nvl72#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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