NVIDIA Blackwell B100 vs B200 Comparison: What is the difference?
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The NVIDIA B100 and NVIDIA B200 are NVIDIA Blackwell–generation data-center GPUs designed for large-scale AI training and inference. They replace Hopper-based GPUs (H100, H200) in new high-end deployments.
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They look similar on paper, but they are not meant for the same type of customer or data center.
Comparison Table - NVIDIA Blackwell B100 vs B200
Topic | NVIDIA B100 | NVIDIA B200 |
Architecture | Blackwell | Blackwell |
Product tier | High-end data-center GPU | Flagship data-center GPU |
Primary focus | Training and inference at scale | Very large-scale training and inference |
Compute capability | Very high | Higher than B100 |
Tensor performance | Excellent | Higher sustained tensor throughput |
Memory type | HBM3e | HBM3e |
Memory capacity | High HBM3e capacity | Up to ~192 GB HBM3e (platform-dependent) |
Memory bandwidth | Multi-TB/s class | Higher sustained bandwidth than B100 |
Power envelope (SXM) | ~700 W class (platform-dependent) | ~1,000 W class (platform-dependent) |
Clocking behavior | Balanced for performance and efficiency | Tuned for maximum throughput |
Cooling requirement | Liquid cooling common | Liquid cooling effectively required |
Form factor | SXM | SXM |
GPUs per server | Up to 8 (HGX / DGX platforms) | Up to 8 (HGX / DGX platforms) |
Multi-GPU scaling | NVLink + NVSwitch | NVLink + NVSwitch (optimized for larger scale) |
Cluster efficiency | Strong | Higher at very large cluster scale |
Typical deployment | Enterprise AI, large training clusters | Hyperscale and frontier-scale AI data centers |
Best fit | High performance with better power efficiency | Fastest time-to-train and maximum throughput |
NVIDIA B100 and NVIDIA B200 are both high-end enterprise AI GPUs. B100 is easier to deploy and still very powerful. B200 is built for data centers that want maximum performance at scale. Both require dedicated AI servers and cannot be used as simple GPU upgrades.
Architecture - NVIDIA Blackwell B100 vs B200
Both GPUs are built on the Blackwell architecture from NVIDIA.
What Blackwell brings:
Much higher AI throughput than Hopper
Native support for FP4, FP8, FP16, BF16
Better multi-GPU scaling with NVLink
Optimized for very large models
NVIDIA B100 and NVIDIA B200 share the same architecture. The difference is scale and power, not features.
Raw Compute Performance - NVIDIA Blackwell B100 vs B200
Area | NVIDIA B100 | NVIDIA B200 |
AI compute | Very high | Higher |
Training speed | Excellent | Faster |
Inference throughput | Excellent | Higher at scale |
Multi-GPU efficiency | Strong | Stronger |
What this means in practice:
NVIDIA B100 already outperforms NVIDIA H100/H200 by a large margin
NVIDIA B200 is tuned for maximum throughput, especially in large clusters
If your workload scales across many GPUs, NVIDIA B200 pulls ahead faster.
Memory - NVIDIA Blackwell B100 vs B200
Feature | NVIDIA B100 | NVIDIA B200 |
Memory type | HBM3e | HBM3e |
Capacity | Up to ~192 GB | Up to ~192 GB |
Bandwidth | ~8 TB/s | ~8 TB/s |
Both can handle:
Very large LLMs
Large batch sizes
Full model residency in GPU memory
Difference: NVIDIA B200 extracts more effective performance from similar memory thanks to higher compute and better scaling.
Power and Cooling - NVIDIA Blackwell B100 vs B200
Aspect | NVIDIA B100 | NVIDIA B200 |
Typical power draw | ~700 W | ~1,000 W |
Cooling needs | High | Very high |
Data-center readiness | Easier | Demanding |
Real-world impact:
NVIDIA B100 fits more data centers without infrastructure changes
NVIDIA B200 often requires:
Liquid cooling
High-capacity PDUs
Modern AI-ready racks
If power or cooling is limited, NVIDIA B100 is usually the safer choice.
Form Factor & Compatibility - NVIDIA Blackwell B100 vs B200
This is where many people get confused.
Not PCIe GPUs
NVIDIA B100 and NVIDIA B200 are SXM GPUs
They do not go into standard PCIe GPU servers
Required platform
SXM GPU sockets
NVLink + NVSwitch
HGX or DGX-class systems
You cannot upgrade an existing GPU server to B100/B200. These GPUs require purpose-built AI servers.
How Many GPUs Fit in One Server - NVIDIA Blackwell B100 vs B200
Server type | GPU count |
Typical enterprise AI node | 8 GPUs |
Rack-scale systems | 64–72+ GPUs |
Most deployments use:
8× NVIDIA B100 or 8× NVIDIA B200 per server
All GPUs fully connected with NVLink
This allows the server to behave like one large accelerator, not eight separate GPUs.
Common Server Platforms Used - NVIDIA Blackwell B100 vs B200
NVIDIA B100 and NVIDIA B200 are mainly used in HGX-based and DGX-class AI servers, such as:
NVIDIA platforms
DGX B200 (8-GPU system)
Rack-scale NVLink systems (72-GPU class)
OEM enterprise AI servers
Supermicro HGX Blackwell servers
Lenovo ThinkSystem AI platforms
Dell PowerEdge AI (XE series)
ASUS and Gigabyte AI servers
Common traits of these systems:
4U–10U chassis
Liquid cooling
Dual high-core CPUs
InfiniBand or high-speed Ethernet
Typical Use Cases - NVIDIA Blackwell B100 vs B200
NVIDIA B100 – Best fit when:
Power and cooling are limited
You want Blackwell performance without extreme density
You are building a balanced AI cluster
Cost efficiency per rack matters
NVIDIA B200 – Best fit when:
Maximum performance is the goal
You train very large models
You run inference at massive scale
Your data center is built for AI (power + cooling)
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