B100 vs B200: Which NVIDIA blackwell GPU is right for your AI workloads?

When working with the latest technology, the GPU you choose determines the size of your models, their efficiency, and the speed at which you can scale. The NVIDIA B100 is already a massive leap over Hopper, bringing the new Blackwell architecture to AI training and inference. But with the launch of the B200, NVIDIA is raising the bar again.

The B200 keeps the same Blackwell foundation as the B100, but pushes it further with higher compute density, more memory bandwidth, and stronger multi-GPU scaling. That makes it especially relevant for teams training trillion-parameter models or running inference at a global scale.

This article breaks down the key differences, with a focus on compute, memory, efficiency, and scaling, so you can see where each GPU fits in your stack.

If you’re short on time, here’s a quick look at how the B100 and B200 compare side by side.

The B100 was the first GPU launched on NVIDIA’s Blackwell architecture. It uses a dual-die design connected by NV-HBI and packs 192 GB of HBM3e memory with 8 TB/s bandwidth.

Key features include:

• Dual-die GB100 design with over 200 billion transistors
• 192 GB of HBM3e memory with up to 8 TB/s bandwidth
• 5th-gen NVLink, delivering 1.8 TB/s per GPU
• Strong tensor performance across FP4, FP6, FP8, FP16, and FP64

This combination makes the B100 a versatile choice for both training and inference, particularly for teams looking to transition from Hopper to Blackwell with balanced compute and memory.

The B200 is NVIDIA’s most powerful Blackwell GPU, offering higher throughput across every precision level compared to the B100. Unlike the dual-die B100, the B200 is a single-die design, making it more efficient and better suited for large-scale clusters.

Highlights include:

• 192 GB of HBM3e memory at up to 8 TB/s bandwidth
• Higher FP4/FP8 performance (up to 18 PFLOPS sparse)
• 40 TFLOPS in FP64 for scientific and HPC workloads
• Same NVLink bandwidth as the B100, but with better efficiency per watt

The result is a GPU built to maximize throughput in both AI and HPC tasks, particularly for inference at scale and future LLM deployments.

We’ve seen what the B100 and B200 can do individually, but the real question is how they compare head-to-head. Both are built on NVIDIA’s Blackwell architecture, yet the B200 refines it with key upgrades in compute density, memory bandwidth, and multi-GPU scaling. These differences don’t just show up in benchmarks; they matter in practice, whether you’re training trillion-parameter models, fine-tuning smaller LLMs, or serving them at scale.

Let’s break it down.

Both GPUs are built on Blackwell’s dual-chip architecture, but the B200 packs more CUDA cores and Tensor Cores. This translates into higher peak FLOPS and stronger performance in FP8 and FP4 precision formats that matter for LLM training and inference.

The B100 supports HBM3e, while the B200 takes it further with even higher bandwidth and larger memory pools per GPU. This means the B200 handles bigger context windows and batch sizes without offloading to slower system memory.

Although both sit within the same broad TDP envelope, the B200 delivers more compute per watt, thanks to architectural optimizations and improved scheduling. That makes it more cost-efficient for long-running jobs.

The B100 already supports 1.8 TB/s of NVLink bandwidth, but the B200 improves GPU-to-GPU interconnect efficiency, which helps when you’re scaling across massive clusters.

Because both are built on Blackwell, they share the same CUDA, cuDNN, and framework compatibility. Upgrading from B100 to B200 doesn’t require workflow changes, but you get performance gains “for free.”

Once you’ve looked at features and performance, the next question is cost. Cloud pricing isn’t just about the hourly rate; it reflects how efficiently each GPU can complete your workloads.

On Northflank, here’s what the current hourly rates look like (September 2025):

The B100 is technically the entry point to Blackwell, but it isn’t widely available. Most cloud providers have skipped it in favor of B200, so you may not find B100 instances at all. The B200 comes at a premium, but with more memory and higher throughput, it can shorten training cycles and lower the total cost of running large-scale workloads.

If you’re ready to try the B200, availability can be tricky. Most cloud providers skipped the B100 entirely and went straight to B200, so you won’t usually find B100 instances at all.

On Northflank, you can spin up B200 GPUs with transparent hourly pricing and no hidden commitments. Whether you’re fine-tuning an LLM or scaling distributed training, you can launch a GPU instance in minutes or book a demo to explore custom setups.

By now, you’ve seen how the B100 delivers balanced performance as the baseline Blackwell GPU, while the B200 pushes compute throughput further without changing the memory configuration. The right choice comes down to workload demands and budget.

The B200 is not a reinvention of Blackwell but an evolution that improves compute performance across the board. By boosting throughput and simplifying architecture, it enables faster inference, stronger HPC workloads, and smoother scaling for the largest models.

For teams pushing the frontier with cutting-edge workloads, the B200 will feel like a necessary upgrade. For those balancing cost and performance, the B100 remains a powerful entry point into Blackwell.

With Northflank, you can access GPUs on demand. Start with B100s today and seamlessly scale to B200s as your models grow. You can launch GPU instances in minutes or book a demo to see how the platform fits your workflow.