NVIDIA H200 vs. B200: Comparing Datacenter-Grade Accelerators

If you're working at the cutting edge of AI and high-performance computing (HPC), NVIDIA's H200 and B200 GPUs are likely already very much on your radar. They're each designed to handle the massive compute demands of datacenter-class workloads – such as training trillion-parameter models and running high-speed inference at scale.

While both GPUs deliver incredible power, it's crucial to understand the differences between the two to make the right choice for your needs.

Today, we'll explore how the H200 and B200 measure up against each other and how each fits into real-world deployments. Which one is right for you? Let's take a look.

NVIDIA H200: Hopper-Based Heavyweight

Building on the foundation laid by the H100, the NVIDIA H200 is based on the same Hopper architecture while bringing substantial improvements in memory capacity and bandwidth. This means fewer bottlenecks, an advantage that becomes increasingly important as you move further into enterprise scale.

The H200 nearly doubles the memory of its predecessor, boasting 141 GB of HBM3e memory and 4.8 TB/s of bandwidth – delivering about 1.4X faster data access. MLPerf benchmarks using Llama 2 70B demonstrated the H200's power: it reached over 31,000 tokens per second, about 45% faster than the H100.

With the same fourth-gen NVLink, PCIe Gen5, MIG support, and full Tensor Core and Transformer Engine capabilities, the H200 is a solid next step for existing Hopper-based deployments. You don't have to rethink your entire infrastructure; you just get more horsepower.

To sum up, here are some of the most notable features of the H200:

• 141 GB of HBM3e memory at 4.8 TB/s – almost double the memory of the H100 with 1.4X the bandwidth.

• Up to 1.9X improved LLM inference performance, with more tokens per second and higher throughput for gen AI workloads compared to its predecessor.

• 50% lower energy use and total cost of ownership (TCO) over the H100 thanks to improved energy efficiency and thermal management.

• Drop-in compatible with existing Hopper infrastructure, so that little to no reconfiguration is required.

In short, the H200 represents the very best of the Hopper-based lineup. When it was introduced in late 2024, it set a new standard for memory-intensive and high-throughput workloads in datacenter GPUs – and it's still one of the most powerful GPUs on the market today.

The B200, on the other hand, is an entire generational leap forward.

NVIDIA B200: The New Generation

Built on NVIDIA's advanced Blackwell architecture, the B200 features 192 GB of ultra-fast HBM3e memory with 8.0 TB/s of memory bandwidth. Those are some staggering numbers, even compared to the H200's respectable 141 GB at 4.8 TB/s.

The Blackwell architecture also introduces fifth-gen Tensor Cores and dual Transformer Engines, which are particularly useful for AI workloads with long context windows and heavy token parallelism. The B200 scales much more efficiently than the H200 for the latest and most advanced model classes.

In fact, scaling is a major area where the B200 stands out. With fifth-gen NVLink, its inter-GPU bandwidth reaches 1.8 TB/s, enabling considerably faster node-to-node communication than Hopper at 900 GB/s.

Of course, this may not matter to you very much if you're only using a single card. At cluster scale, however, or in multi-GPU systems like the DGX B200 (which connects eight B200 GPUs together), the improvements in distributed training efficiency really make a difference.

The B200 truly sits at the frontier of performance. Its raw capacity and speed can support next-gen inference pipelines and AI workloads at hyperscale. According to NVIDIA, it delivers 3X the training performance and 15X the inference performance of the previous generation.

These gains do come at a cost in power draw. The B200 has a max thermal design power (TDP) of 1000 W compared to the H200 at 700 W – which means it also requires far more robust cooling solutions. But the extra power consumption may just be worth it for the boost in performance.

To recap, a few advantages of the B200 include:

• 192 GB of HBM3e memory at 8.0 TB/s – ultra-high capacity that's well suited for trillion-parameter models and allows training of AI models entirely in GPU memory.

• Fifth-gen Tensor Cores and dual Transformer Engines for higher efficiency in FP8, FP16, and mixed precision performance, especially in long-context and multi-modal workloads.

• NVLink 5 interconnect with up to 1.8 TB/s GPU-to-GPU bandwidth for multi-node scaling.

• CUDA compatibility with support for CUDA 12.4+, enabling teams to benefit from Blackwell's optimizations while still running familiar workflows.

The B200 was built to shatter performance records. As you can imagine, it has an eyewatering price tag to match. Nonetheless, it can be helpful to compare the B200 and H200 based on specs alone.

H200 vs. B200: Key Specs Compared

Here's a side-by-side look at some key features of the H200 and B200:

Feature	H200	B200
Architecture	Hopper	Blackwell
Memory	141 GB HBM3e	192 GB HBM3e
Memory Bandwidth	4.8 TB/s	8 TB/s
CUDA Cores	16,896	16,896 x2
Tensor Cores	528 (4th Gen)	528 x2 (5th Gen)
Boost Clock	1.98 GHz	1.98 GHz
Transformer Engine	Single	Dual
Form Factor	SXM	SXM
Interconnect	NVLink 4 (900 GB/s)	NVLink 5 (1.8 TB/s)
CUDA Compatibility	CUDA 12.2+	CUDA 12.4+
Precision Formats	FP8, FP16/BF16, TF32, FP32, FP64, INT8	FP4, FP6, FP8, FP16/BF16, TF32, FP32, FP64, INT8
Multi-Instance GPUs	Up to 7	Up to 7
Max Thermal Design Power (TDP)	700 W	1000 W
Recommended Power Supply	1100 W	1400 W

Choosing the right GPU often comes down to more than just specs, however.

Use Cases: Which GPU Should You Choose?

While both the H200 and B200 deliver game-changing performance capabilities, they each have distinct advantages depending on your needs.

You should consider the H200 if you prefer:

• A high-performance GPU ideal for fine-tuning LLMs and high-throughput inference at the enterprise level without getting into exascale territory.

• A proven workhorse that balances strong performance with lower TDP and cost.

• Being able to seamlessly upgrade from existing Hopper deployments without overhauling infrastructure.

Or the B200 might be the right choice if you need:

• To push model boundaries and handle long-context and/or multi-modal foundation model training at scale.

• The absolute maximum throughput and efficiency for multi-GPU or multi-node clusters.

• A future-proof investment for infrastructure built to support the next generation of cutting-edge AI and AGI.

Conclusion

At Vast.ai, we know that the choice between GPUs like the H200 and B200 can depend on a lot more than just performance. Access and cost are important factors, as well.

The H200 and B200 represent the very best of NVIDIA's current lineup, and they're among the most powerful and expensive GPUs in the world today. Buying them outright is undoubtedly out of reach for most teams.

That's what we're here for. With Vast, you can rent high-end GPUs like the A100, H100, H200, and soon the B200, with on-demand pricing and spot instances – and pay only for what you use.

On average, Vast users save 5–6X on GPU compute compared to traditional cloud providers. You'll find a variety of options on our platform to fit your needs, so you can focus on what really matters: your work, not your budget.

Explore Vast today and enjoy enterprise-grade compute without enterprise-level costs.