H200 vs B200: Hopper or Blackwell for your next GPU server?

The H200 vs B200 decision spans a GPU generation. The H200 is the high-memory version of Hopper: 141 GB of HBM3e at 4.8 TB/s. The B200 is Blackwell: 180 GB of HBM3e at 8 TB/s, native FP4, NVLink 5 and, per NVIDIA's published specifications, about 2.3 times the dense FP8 and BF16 throughput. It also costs $4,590 a month against $3,090. Whether the premium pays off depends on what limits your workload: memory capacity, memory bandwidth or compute.

H200 vs B200: the short answer

Pick the B200 if you can keep it busy: training, fine-tuning at scale, high-concurrency serving, or models that need more than 141 GB per GPU. Per dollar, it gives you more compute and more bandwidth than the H200.

Pick the H200 if your model fits in 141 GB and your traffic does not saturate Hopper's compute. It costs $1,500 less per GPU and $11,000 less per 8-GPU server, and it is the cheaper of the two per GB of memory. For 70B-class inference in FP8 at moderate traffic, that makes it the better buy.

Memory: what fits on each GPU

The B200 has 39 GB more per GPU; NVIDIA's HGX B200 exposes 180 GB of the 192 GB of HBM3e physically on each GPU. That margin matters most for 70B-class models in BF16, which an H200 cannot serve on one GPU. The table subtracts approximate weights from capacity. Keep 10–30% of what remains for runtime overhead; the rest goes to the KV cache.

On 8-GPU servers, both generations hold very large open models in FP8 on a single machine. The B200 leaves more room for the KV cache, which means longer contexts and more concurrent requests per server. Training is tighter: full fine-tuning of a 70B model with Adam needs roughly 1.1–1.4 TB before activations, which leaves no practical room on 8× H200 (1,128 GB) without offloading and is at the edge of 8× B200 (1,440 GB). The VRAM calculator runs these numbers for your own model and context length.

Compute and precision: FP8, BF16 and FP4

NVIDIA lists the H200 at 1,979 TFLOPS of dense FP8 and 989 TFLOPS of dense BF16. The B200 figures are 4.5 and 2.25 PFLOPS, about 2.3 times higher, and Blackwell adds FP4 at 9 PFLOPS dense. These are peak vendor numbers that real jobs reach only in part on either GPU, but the ratio is a fair guide for work that is genuinely compute-bound: large-batch training, prefill on long prompts and serving many users at once. Both GPUs train and serve in FP8 through NVIDIA's Transformer Engine; Blackwell's second generation of it adds FP4.

FP4 halves weight memory again compared with FP8, and NVIDIA lists twice the FP8 peak for it on Blackwell. It is not free: output quality depends on the model and on how it was quantized, so check your own evaluation set before switching. On an H200 you can still store weights in 4 bits with weight-only quantization, which saves memory, but the math runs at higher precision. Our FP8 and FP4 guide covers the trade-offs.

Bandwidth and NVLink: serving and multi-GPU scaling

Token generation reads weights and KV cache from memory at every step, so at small and medium batch sizes it is bound by bandwidth. The B200's 8 TB/s is 67% more than the H200's 4.8 TB/s. That is the most direct improvement for interactive serving, where each user waits on decoding, and it applies whether or not you use FP4.

Between GPUs, NVLink 5 gives each B200 1.8 TB/s against 900 GB/s for NVLink 4 on the H200. Tensor parallelism exchanges activations at every layer, so the faster link cuts the time eight GPUs spend waiting on each other. Between servers, our HGX B200 and HGX H200 use the same 8× 400G ConnectX-7 networking, and both hosts pair two Xeon CPUs (112 cores in total) with 2 TB of DDR5 and 8× 3.84 TB of NVMe; the difference is the GPUs. Our article on NVLink vs PCIe explains why the link matters.

Cost per month and what $1,000 buys

A single B200 costs $4,590 a month, $1,500 more than an H200 at $3,090 (49%). An 8-GPU HGX B200 costs $34,900 against $23,900 for the HGX H200, $11,000 more (46%). Normalized per $1,000 of monthly spend on single-GPU plans:

The B200 is the better value for bandwidth and compute, the H200 for capacity. Two worked examples. A 70B model in FP8 at moderate traffic fits on one H200 with plenty of KV cache; if one H200 keeps up, a B200 adds $1,500 a month of headroom you do not use. If instead you are compute-bound and would otherwise run two H200s ($6,180), one B200 ($4,590) offers more vendor-listed FP8 compute than both combined (4.5 against 3.96 PFLOPS), though less total memory (180 against 282 GB) and bandwidth (8 against 9.6 TB/s).

For training, compare cost per run rather than cost per month. The HGX B200 costs about 1.46 times as much as the HGX H200, while NVIDIA lists about 2.3 times the dense compute and the bandwidth ratio is about 1.67. Both sit above the price ratio, so if your job scales with compute or bandwidth, a B200 server that you keep busy is the cheaper way to finish the same work. A B200 that waits on data loading, small batches or an idle queue loses that edge.

Software, stock and how to decide

Hopper is the older platform, so almost every framework, kernel library and container already supports it. Blackwell needs CUDA 12.8 or later and framework builds compiled for it. Current releases of the major frameworks and inference engines support it, but containers pinned to older CUDA versions, or kernels hand-tuned for Hopper, may need a rebuild or a port. See NVIDIA drivers and CUDA for what our images include. Both GPUs are in stock as single GPUs and 8-GPU servers.

• Model plus KV cache under about 125 GB at moderate traffic: H200.
• Between about 125 and 160 GB per GPU, or 70B models in BF16: B200.
• Compute-bound training or busy serving that can use the extra throughput: B200.
• Even more memory per GPU: compare the B200 with the 288 GB B300 in our B200 vs B300 comparison.