NVIDIA B200, HGX B200, DGX B200 & GB200 Comparison: What Is the Difference?
- 2 days ago
- 13 min read
The NVIDIA B200 GPU is part of NVIDIA’s Blackwell generation and is one of the most important data center GPUs after H100 and H200. But the naming can be confusing. You may see B200, HGX B200, DGX B200, GB200, GB200 NVL72, and sometimes Blackwell Ultra or GB300.
These names do not all mean the same thing. B200 is the GPU. HGX B200, DGX B200, GB200, and GB200 NVL72 are platforms or systems built around Blackwell GPUs.
NVIDIA B200 GPUs & GPU Servers
In Stock: B200 / HGX B200 / DGX B200 / GB200 AI Servers
This article compares NVIDIA B200, HGX B200, DGX B200, GB200, and GB200 NVL72 technically, explains the bandwidth architecture, covers real server configurations, and helps you decide which configuration fits your infrastructure and workload.
Full Comparison: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA Blackwell Product Comparison (NVIDIA B200, HGX B200, DGX B200 & GB200)
Product | What it is | Best for |
NVIDIA B200 GPU | Blackwell GPU module | AI training, inference, HPC |
NVIDIA HGX B200 | 8-GPU OEM platform | AI servers from OEM vendors |
NVIDIA DGX B200 | Complete NVIDIA 8-GPU system | Enterprise AI infrastructure |
NVIDIA GB200 | Grace CPU + 2 Blackwell GPUs | CPU-GPU connected workloads |
NVIDIA GB200 NVL72 | Full rack-scale AI system | AI factories and very large clusters |
Technical Comparison (NVIDIA B200, HGX B200, DGX B200 & GB200)
Product | GPU memory | Bandwidth | Interconnect | Infrastructure |
B200 GPU | 180GB HBM3e per GPU | Up to 8TB/s per GPU | NVLink, platform dependent | Needs a compatible Blackwell server platform |
HGX B200 | 8 × 180GB, 1.44TB total | Up to 64TB/s total | NVLink + NVSwitch | OEM server platform |
DGX B200 | 1.44TB total | 64TB/s total | 14.4TB/s aggregate NVLink | NVIDIA complete 10U system |
GB200 | Platform dependent | Platform dependent | NVLink-C2C + NVLink | MGX or rack-scale system design |
GB200 NVL72 | 13TB+ class HBM3e rack memory | 576TB/s class rack bandwidth | 72-GPU NVLink domain | Full liquid-cooled rack-scale infrastructure |
Power and Scaling (NVIDIA B200, HGX B200, DGX B200 & GB200)
Product | Power profile | Scale strategy |
B200 GPU | Up to around 1000W per GPU, depending on configuration | Scale-up GPU |
HGX B200 | Very high server-level power demand | Scale-up 8-GPU server |
DGX B200 | Around 14.3kW max system power | Enterprise scale-up system |
GB200 | Platform dependent | CPU + GPU scale-up |
GB200 NVL72 | Rack-scale power and liquid cooling | Rack-scale AI factory |
NVIDIA B200, HGX B200, DGX B200, GB200, and GB200 NVL72 are all connected to the Blackwell generation, but they are not the same product.
The simple difference:
B200 is the GPU.
HGX B200 is the 8-GPU platform used by server vendors.
DGX B200 is NVIDIA’s own complete 8-GPU server.
GB200 combines Grace CPU and Blackwell GPUs.
GB200 NVL72 is a full rack-scale Blackwell system.
Quick Comparison: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA Blackwell product | Best for | GPU memory | Power / infrastructure |
B200 GPU | AI training, inference and HPC inside compatible servers | 180GB HBM3e per GPU | Up to 1000W per GPU |
HGX B200 | OEM 8-GPU AI servers | 8 × 180GB / 1.44TB total | High-power AI server platform |
DGX B200 | Complete NVIDIA 8-GPU enterprise AI system | 1,440GB total | Around 14.3kW max system power |
GB200 | Workloads needing tight Grace CPU + Blackwell GPU connection | Platform dependent | Used in advanced MGX / rack-scale designs |
GB200 NVL72 | Very large AI training and inference clusters | 13TB+ class HBM3e rack memory | Full rack-scale liquid-cooled system |
Technical Differences: NVIDIA B200, HGX B200, DGX B200 & GB200
Platform | Memory type | Memory bandwidth | GPU-to-GPU / system bandwidth |
B200 GPU | HBM3e | 7.7–8TB/s per GPU | NVLink, platform dependent |
HGX B200 | HBM3e | Around 64TB/s total | NVLink + NVSwitch |
DGX B200 | HBM3e | 64TB/s total | 14.4TB/s aggregate NVLink |
GB200 | HBM3e | Platform dependent | NVLink-C2C between Grace CPU and Blackwell GPU |
GB200 NVL72 | HBM3e | 576TB/s class rack bandwidth | 72-GPU NVLink domain |
The main difference is not just the GPU, but the system architecture. With H200, buyers often compare H200 SXM5 and H200 NVL. With B200, the discussion is more about platforms like HGX B200, DGX B200, and GB200. It is less a standalone GPU card decision and more a full system decision.
Decision Shortcut: NVIDIA B200, HGX B200, DGX B200 & GB200
If you only need one thing from this article:
Actual GPU name → NVIDIA B200
8-GPU OEM server platform → HGX B200
8-GPU complete NVIDIA server → DGX B200
Grace CPU + Blackwell GPU architecture → GB200
Full rack-scale Blackwell AI infrastructure → GB200 NVL72
Large enterprise training / fine-tuning / inference → DGX B200 or HGX B200
AI factory / hyperscale deployment → GB200 NVL72
Standard PCIe server upgrade → B200 is probably not the right comparison
Everything else in this article is the technical reasoning behind that table.
What Is the Difference Between B200, HGX B200, DGX B200 and GB200?
NVIDIA B200 is the actual Blackwell Tensor Core GPU, usually used as an SXM6 data center module inside high-performance AI platforms, not as a simple PCIe card for any standard server. HGX B200 is the 8-GPU platform used by OEMs like Dell, Supermicro, Lenovo, and HPE to build Blackwell servers. DGX B200 is NVIDIA’s own complete 8-GPU AI system with CPUs, memory, NVMe storage, networking, NVSwitch, software, and enterprise support. GB200 is different because it combines one Grace CPU with two Blackwell GPUs using NVLink-C2C, while GB200 NVL72 takes this further as a full rack-scale system with 36 Grace CPUs and 72 Blackwell GPUs for very large AI training and inference workloads.
The simple difference:
Name | What it is | Best for |
B200 | Blackwell GPU module | AI training, inference, and HPC |
HGX B200 | OEM 8-GPU server platform | High-end enterprise AI servers |
DGX B200 | Complete NVIDIA 8-GPU AI system | Turnkey enterprise AI infrastructure |
GB200 | Grace CPU + 2× Blackwell GPUs | CPU + GPU accelerated AI workloads |
GB200 NVL72 | Full rack-scale Blackwell system | AI factories and massive AI clusters |
What Is the Difference Between NVIDIA B200 and GB200?
NVIDIA product | Main purpose | Configuration |
B200 | Blackwell GPU for AI compute | One Blackwell GPU module |
GB200 | Grace Blackwell Superchip for CPU + GPU acceleration | One Grace CPU + two Blackwell GPUs |
B200 is the GPU, while GB200 is the CPU+GPU platform. If a supplier says “GB200,” they usually mean a Grace Blackwell system where NVIDIA Grace CPU and Blackwell GPUs are tightly connected, not a single standalone GPU. Buyers should ask for B200 when they mean the GPU itself, and GB200 when they mean rack-scale AI infrastructure such as GB200 NVL72.
NVIDIA B200, HGX B200, DGX B200 & GB200: Bandwidth Architecture in Detail
Metric | Bandwidth / scale | Topology |
B200 GPU memory bandwidth | 7.7–8TB/s per GPU | HBM3e on one GPU |
DGX B200 memory bandwidth | 64TB/s total | 8-GPU system |
DGX B200 NVLink bandwidth | 14.4TB/s aggregate | NVLink + NVSwitch |
GB200 NVLink-C2C | CPU-to-GPU connection | Grace CPU + Blackwell GPUs |
GB200 NVL72 NVLink domain | 72 GPUs | Rack-scale NVLink architecture |
GB200 NVL72 memory bandwidth | 576TB/s class rack bandwidth | Rack-scale HBM3e bandwidth |
B200 is built for much stronger AI performance than previous Hopper-generation systems, especially for large LLM training, fine-tuning, real-time inference, long context windows, mixture-of-experts models, multi-GPU parallelism, AI factory infrastructure, HPC, and high GPU utilization across many GPUs. For smaller inference jobs, H100 or H200 systems may still be enough, but for very large training, heavy inference, and future AI infrastructure, B200 and GB200 are the next step.
NVIDIA B200 GPUs & GPU Servers
In Stock: B200 / HGX B200 / DGX B200 / GB200 AI Servers
NVIDIA B200, HGX B200, DGX B200 & GB200: Full Technical Specifications
NVIDIA Blackwell architecture
GPU: NVIDIA B200 Tensor Core GPU
Form factor: SXM6
Memory: HBM3e
Tensor Cores: 5th generation
Transformer Engine: 2nd generation
FP4 support: Yes
FP8 support: Yes
MIG support: Yes
NVLink generation: 5th generation in B200 / GB200 systems
Important note: exact performance numbers vary depending on precision, sparsity, power profile, cooling design, server configuration, and whether the value is listed per GPU or per full system.
Tensor Core Performance: NVIDIA B200 GPU
Precision | Dense performance | With sparsity |
FP64 | 37 TFLOPS | — |
FP64 Tensor Core | 37 TFLOPS | — |
FP32 | 75 TFLOPS | — |
TF32 Tensor Core | 1.1 PFLOPS | 2.2 PFLOPS |
BF16 Tensor Core | 2.25 PFLOPS | 4.5 PFLOPS |
FP16 Tensor Core | 2.25 PFLOPS | 4.5 PFLOPS |
FP8 Tensor Core | 4.5 PFLOPS | 9 PFLOPS |
INT8 Tensor Core | 4.5 POPS | 9 POPS |
FP4 Tensor Core | 9 PFLOPS | 18 PFLOPS |
The major change with Blackwell is FP4 support, which can increase large AI inference throughput, reduce cost per token, and make B200 more relevant for future workloads optimized for FP4 and FP8.
VRAM & Memory Bandwidth: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA platform | GPU memory | Memory type | Memory bandwidth |
B200 GPU | 180GB per GPU | HBM3e | 7.7–8TB/s per GPU |
HGX B200 | 8 × 180GB / 1.44TB total | HBM3e | Around 64TB/s total |
DGX B200 | 1,440GB total | HBM3e | 64TB/s total |
GB200 NVL72 | 13TB+ rack total | HBM3e | 576TB/s rack-class bandwidth |
B200 offers more GPU memory than H100 and H200, with 180GB HBM3e and around 7.7–8TB/s bandwidth per GPU, which helps with larger LLMs, longer context windows, bigger batch sizes, fine-tuning, memory-heavy inference, model parallel workloads, and multi-tenant AI platforms; in DGX B200, NVIDIA lists 8 Blackwell GPUs with 1,440GB total GPU memory and 64TB/s total HBM3e bandwidth.
GPU-to-GPU Interconnect: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA platform | Interconnect |
B200 GPU | NVLink, platform dependent |
HGX B200 | NVLink + NVSwitch |
DGX B200 | 14.4TB/s aggregate NVLink bandwidth |
GB200 | NVLink-C2C between Grace CPU and Blackwell GPUs |
GB200 NVL72 | 72-GPU NVLink domain |
The interconnect is one of the biggest reasons to choose B200 or GB200, because strong GPU-to-GPU bandwidth matters for large model training, model and tensor parallelism, mixture-of-experts workloads, advanced inference, HPC, scientific computing, and multi-GPU research clusters — but if your workload does not need this, H100 or H200 can still make more financial sense.
Power Draw: NVIDIA B200, HGX B200, DGX B200 & GB200
Platform | Power / infrastructure |
B200 GPU | Up to around 1000W per GPU |
DGX B200 | Around 14.3kW max system power |
GB200 NVL72 | Rack-scale power and liquid cooling |
B200 systems need serious power and cooling planning, because this is not like adding a low-power PCIe GPU to a normal enterprise server. Before buying, check rack power, PDU capacity, cooling, airflow, liquid cooling needs, power cables, redundancy, and overall data center readiness. This matters even more in the refurbished market, because a good GPU price is useless if the system cannot be powered or cooled properly.
Server Compatibility: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA platform | Server compatibility |
B200 GPU | Requires a compatible Blackwell server platform |
HGX B200 | Used in OEM 8-GPU AI servers |
DGX B200 | NVIDIA complete 10U AI system |
GB200 | Requires a Grace Blackwell platform design |
GB200 NVL72 | Full rack-scale, liquid-cooled infrastructure |
You cannot treat B200 like a normal PCIe GPU upgrade, because the exact server platform matters: GPU baseboard, SXM6 support, power, cooling, firmware, CPUs, memory, NVMe storage, networking, NVLink / NVSwitch topology, vendor support, warranty, and test reports must all match. This is why B200 is usually bought as a complete server or platform, not as a loose GPU.
MIG Support: NVIDIA B200 GPU
B200 supports Multi-Instance GPU, which lets one physical GPU be split into smaller isolated instances for different users, teams, or workloads.
For example, one B200 can be used for:
MIG setup | Typical use case |
Several small GPU instances | Many small inference workloads |
Medium GPU instances | Internal enterprise AI teams |
One full GPU | Large training or heavy inference workload |
MIG is useful for AI cloud providers, Kubernetes GPU clusters, enterprise AI platforms, research teams, multi-tenant inference, GPU-as-a-Service, and AI labs because it helps improve GPU utilization.
NVIDIA B200, HGX B200, DGX B200 & GB200: Scale-Up vs Rack-Scale
B200, HGX B200, and DGX B200 are best for scale-up inside one powerful server, while GB200 NVL72 is built for rack-scale AI where many Blackwell GPUs work together in one large NVLink domain.
Strategy | Best option | Best for |
Scale-up GPU server | HGX B200 | 8-GPU OEM AI servers |
Turnkey enterprise AI | DGX B200 | Complete NVIDIA AI system |
CPU + GPU acceleration | GB200 | Grace CPU + Blackwell GPU workloads |
Rack-scale AI | GB200 NVL72 | Very large training and inference |
Flexible enterprise deployment | HGX B200 / DGX B200 | AI teams, research, fine-tuning, and inference |
Choose HGX B200 or DGX B200 when you want a powerful 8-GPU Blackwell server.
Choose GB200 NVL72 when you are building rack-scale AI infrastructure.
Multi-Instance GPU: NVIDIA B200
MIG on NVIDIA B200 lets one physical GPU be split into smaller isolated instances, so multiple users, teams, or workloads can share GPU resources safely.
For example, one B200 can be used for:
MIG setup | Typical use case |
Small GPU instances | Many small inference workloads |
Medium GPU instances | Medium inference or team workloads |
Full GPU | One large training or heavy inference workload |
B200 with MIG is useful for AI cloud providers, Kubernetes GPU clusters, enterprise AI platforms, research teams, multi-tenant inference, and GPU-as-a-Service environments because users do not always need to reserve a full B200 GPU.
NVIDIA B200 GPU Workloads
NVIDIA B200 / HGX B200 / DGX B200
B200 is a strong choice for training, fine-tuning, inference, data analytics, HPC, and memory-heavy AI workloads, especially when large models, long context windows, high batch sizes, multi-tenant platforms, or a move from H100/H200 to Blackwell require more GPU performance and memory.
NVIDIA GB200 / GB200 NVL72
GB200 and GB200 NVL72 are stronger choices for rack-scale AI infrastructure, especially when many GPUs must work together with very high bandwidth for AI factories, hyperscale AI, large model training, trillion-parameter inference, mixture-of-experts workloads, research supercomputers, GPU cloud platforms, and systems that need to scale beyond one server.
Available NVIDIA B200 Servers and Systems
These are the system types most commonly connected to NVIDIA B200 and GB200 platforms, both from new inventory and the secondary market.
B200 / HGX B200 servers — for 8-GPU training and inference
NVIDIA HGX B200 — OEM 8-GPU platform used by server manufacturers for high-performance AI and HPC systems.
NVIDIA DGX B200 — NVIDIA’s own complete 8-GPU Blackwell system with NVLink, NVSwitch, CPUs, memory, storage, networking, and NVIDIA software stack.
Dell PowerEdge XE-series Blackwell systems — Dell enterprise AI servers based on NVIDIA Blackwell platforms, depending on the exact model and configuration.
Supermicro HGX B200 systems — common in GPU cloud, AI infrastructure, and research environments.
Lenovo ThinkSystem HGX B200 systems — Lenovo GPU platforms for enterprise AI and HPC workloads.
HPE Cray / ProLiant / Blackwell AI systems — high-density AI and HPC platforms, depending on the exact configuration.
GB200 / GB200 NVL72 systems — for rack-scale AI
NVIDIA GB200 Grace Blackwell systems — Grace CPU and Blackwell GPUs connected with NVLink-C2C.
NVIDIA GB200 NVL72 — full rack-scale system with 36 Grace CPUs and 72 Blackwell GPUs.
NVIDIA DGX SuperPOD with GB200 — large-scale NVIDIA AI infrastructure based on Grace Blackwell systems.
OEM GB200 NVL72 systems — rack-scale systems from major server vendors, usually requiring serious data center power, cooling, and networking planning.
Always check the exact B200 or GB200 configuration, including:
GPU form factor
SXM6 compatibility
HGX platform support
power and cooling
liquid cooling requirements
firmware
networking
NVLink / NVSwitch topology
InfiniBand or Ethernet
rack power capacity
warranty
testing
Common Mistakes When Buying NVIDIA B200, HGX B200, DGX B200 & GB200
Thinking B200 is a normal PCIe GPU
B200 is not usually bought like a standard PCIe GPU upgrade.
It is mainly used inside high-end Blackwell platforms such as HGX B200, DGX B200, and GB200 systems.
Confusing B200 and GB200
B200 is the GPU. GB200 is a Grace Blackwell platform that combines Grace CPU and Blackwell GPUs. They are related, but they are not the same product.
Confusing DGX B200 and HGX B200
DGX B200 is NVIDIA’s complete system. HGX B200 is the platform used by OEM server vendors. If you buy from Dell, Supermicro, Lenovo, HPE, or another server vendor, you are usually looking at an HGX B200-based server, not a DGX B200.
Ignoring power and cooling
B200 systems are very high-power systems. A single B200 GPU can draw up to around 1000W depending on the system configuration and power profile. A DGX B200 system is around 14.3kW max system power. Always check rack power, PDU capacity, cooling, airflow, liquid cooling requirements, power cables, and redundant power design.
Buying B200 when H200 would be enough
B200 is powerful, but not every workload needs it.
For many inference workloads, H100 or H200 may still be more practical, cheaper, and easier to source. If your workload does not need Blackwell-level performance, H200 can still be a strong option.
Buying GB200 when a single 8-GPU server would be enough
GB200 NVL72 is rack-scale infrastructure. If your workload fits into one 8-GPU server, DGX B200 or HGX B200 may be much more practical than a full GB200 NVL72 rack.
Forgetting about networking
For serious AI clusters, the GPU is only one part of the system. You also need the right NICs, InfiniBand or Ethernet speed, switches, cables, topology, firmware, and tested ports. A weak network design can limit the value of expensive B200 or GB200 systems.
Not checking support and warranty
B200 and GB200 systems are expensive. Always check warranty, vendor support, firmware access, software support, and whether the system can be maintained in your region. This is especially important in the refurbished and secondary market.
We have NVIDIA B200 GPUs and complete Blackwell AI servers available across HGX B200, DGX B200, and GB200 configurations.
FAQ: NVIDIA B200, HGX B200, DGX B200 & GB200
What is NVIDIA B200?
NVIDIA B200 is a Blackwell-generation data center GPU for AI training, inference, HPC, and memory-heavy workloads. It uses HBM3e memory and is mainly used inside high-performance systems such as HGX B200, DGX B200, and GB200 platforms.
Is NVIDIA B200 a PCIe GPU?
B200 is mainly known as an SXM6 data center GPU module used in high-end Blackwell platforms. It should not be treated like a normal PCIe GPU upgrade for a standard server.
What is HGX B200?
HGX B200 is NVIDIA’s 8-GPU platform for OEM servers. Server vendors use HGX B200 to build high-performance Blackwell AI servers.
What is DGX B200?
DGX B200 is NVIDIA’s own complete 8-GPU Blackwell AI system. It includes the GPUs, CPUs, memory, storage, networking, NVSwitch, software stack, and NVIDIA enterprise support.
What is GB200?
GB200 is a Grace Blackwell platform that combines one NVIDIA Grace CPU with two Blackwell GPUs. It is not just another GPU name.
What is GB200 NVL72?
GB200 NVL72 is a full rack-scale liquid-cooled system with 36 Grace CPUs and 72 Blackwell GPUs connected in a large NVLink domain.
What is the main difference between B200 and GB200?
B200 is the GPU. GB200 combines Grace CPU and Blackwell GPUs into a tightly connected platform.
What is the main difference between DGX B200 and HGX B200?
DGX B200 is NVIDIA’s complete server. HGX B200 is the platform used by OEM server vendors to build their own B200 systems.
How much memory does NVIDIA B200 have?
NVIDIA B200 has 180GB HBM3e memory per GPU.
How much memory bandwidth does NVIDIA B200 have?
NVIDIA B200 is commonly listed with around 7.7TB/s memory bandwidth per GPU. Some system-level sources round this to around 8TB/s.
How much power does NVIDIA B200 use?
NVIDIA B200 has up to around 1000W total graphics power per GPU, depending on the system configuration and power profile.
Does NVIDIA B200 support MIG?
Yes, NVIDIA B200 supports Multi-Instance GPU. It can be split into smaller isolated GPU instances for multi-tenant workloads.
Which NVIDIA B200 system is best for LLM training?
For serious LLM training, HGX B200 or DGX B200 is usually the right starting point.
For very large training clusters, GB200 NVL72 may be the better architecture.
Which NVIDIA B200 system is best for LLM inference?
For enterprise inference, DGX B200 or HGX B200 can be a strong option.
For very large real-time inference and AI factory deployments, GB200 NVL72 is designed for rack-scale performance.
Should I buy B200, DGX B200, HGX B200 or GB200?
Choose B200 when you are talking about the GPU itself.
Choose HGX B200 when you want an OEM 8-GPU server platform.
Choose DGX B200 when you want NVIDIA’s complete 8-GPU system.
Choose GB200 when you need Grace CPU and Blackwell GPUs tightly connected.
Choose GB200 NVL72 when you are building full rack-scale AI infrastructure.
NVIDIA B200 GPUs & GPU Servers
In Stock: B200 / HGX B200 / DGX B200 / GB200 AI Servers
Sources: NVIDIA B200, HGX B200, DGX B200 & GB200
NVIDIA DGX B200 official product page: https://www.nvidia.com/en-us/data-center/dgx-b200/
NVIDIA DGX B200 system documentation: https://docs.nvidia.com/dgx/dgxb200-user-guide/introduction-to-dgxb200.html
NVIDIA GB200 NVL72 official product page: https://www.nvidia.com/en-us/data-center/gb200-nvl72/
NVIDIA HGX Platform official product page: https://www.nvidia.com/en-us/data-center/hgx/
Lenovo ThinkSystem NVIDIA HGX B200 180GB 1000W GPU Product Guide: https://lenovopress.lenovo.com/lp2226-thinksystem-nvidia-b200-180gb-1000w-gpu
Lenovo ThinkSystem NVIDIA HGX B200 180GB 1000W GPU Product Guide PDF: https://lenovopress.lenovo.com/lp2226.pdf
NVIDIA Blackwell Architecture official page: https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
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