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

> H200 vs B200 compared: 141 vs 180 GB, 4.8 vs 8 TB/s, FP4 and NVLink 5. See what fits, what $1,500 more a month buys and when the H200 is the better buy.

Source: https://overgpu.com/compare/h200-vs-b200  
Updated: 2026-10-22  
Publisher: OverGPU (https://overgpu.com/)

**In short:** Pick the B200 when you can use its compute, bandwidth and 180 GB; pick the H200, which costs $1,500 less per GPU and less per GB, when your model fits in 141 GB and Hopper keeps up with your traffic.

## H200 vs B200 specifications

| Specification | NVIDIA H200 | NVIDIA B200 |
| --- | --- | --- |
| Architecture | Hopper | Blackwell |
| GPU memory | 141 GB HBM3e | 180 GB HBM3e |
| Memory bandwidth | 4.8 TB/s | 8 TB/s |
| FP4 | — | 9,000 TFLOPS |
| FP8 | 1,979 TFLOPS | 4,500 TFLOPS |
| BF16 / FP16 | 989 TFLOPS | 2,250 TFLOPS |
| GPU interconnect | NVLink 4 · 900 GB/s | NVLink 5 · 1.8 TB/s |
| Form factor | SXM module · HGX H200 | SXM module · HGX B200 |
| Max power | 700 W | 1,000 W |

## Monthly prices at OverGPU

| Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [NVIDIA H200](https://overgpu.com/gpus/nvidia-h200) | Single GPU | 141 GB HBM3e | 24 vCPU | 240 GB | 2 TB NVMe | 25 Gbps | $3,090 | $4.23 | In stock, delivered in under 10 minutes after payment |
| [8× NVIDIA HGX H200](https://overgpu.com/gpus/nvidia-h200) | 8-GPU server | 8 × 141 GB HBM3e | 2× Xeon 8480+ · 112 cores | 2 TB DDR5 | 8× 3.84 TB NVMe | 8× 400G ConnectX-7 | $23,900 | $4.09 | In stock, delivered in under 10 minutes after payment |
| [NVIDIA B200](https://overgpu.com/gpus/nvidia-b200) | Single GPU | 180 GB HBM3e | 26 vCPU | 256 GB | 2.75 TB NVMe | 25 Gbps | $4,590 | $6.29 | In stock, delivered in under 10 minutes after payment |
| [8× NVIDIA HGX B200](https://overgpu.com/gpus/nvidia-b200) | 8-GPU server | 8 × 180 GB HBM3e | 2× Xeon 8570 · 112 cores | 2 TB DDR5 | 8× 3.84 TB NVMe | 8× 400G ConnectX-7 | $34,900 | $5.98 | In stock, delivered in under 10 minutes after payment |

Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price.

## Choose the H200 if…

- Your model and KV cache fit in 141 GB and you want the lowest cost per GB of GPU memory.
- Your traffic is moderate, so memory capacity matters more to you than raw compute.
- Your stack is pinned to older CUDA releases or Hopper-specific kernels you do not want to port yet.

## Choose the B200 if…

- You need more than 141 GB per GPU, for example for a 70B model in BF16 with room for the KV cache.
- Your workload is compute-bound, such as training or high-concurrency serving.
- You want FP4 inference or twice the NVLink bandwidth for tensor parallelism.

## Overview

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](https://overgpu.com/gpus/nvidia-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](https://overgpu.com/gpus/nvidia-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.

| Model and precision | Weights (approx.) | Free on H200 | Free on B200 |
| --- | --- | --- | --- |
| 70B in FP8, 1 GPU | 70 GB | About 71 GB | About 110 GB |
| 70B in BF16, 1 GPU | 140 GB | About 1 GB: not workable | About 40 GB |
| 70B in FP4, 1 GPU | 35 GB | No FP4 math; 4-bit weight-only formats fit | About 145 GB |
| 405B in FP8, 8 GPUs | 405 GB | About 723 of 1,128 GB | About 1,035 of 1,440 GB |
| 671B MoE in FP8, 8 GPUs | 671 GB | About 457 of 1,128 GB | About 769 of 1,440 GB |

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](https://overgpu.com/tools/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](https://overgpu.com/blog/fp8-fp4-precision-explained) 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](https://overgpu.com/blog/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:

| Per $1,000 a month | H200 | B200 |
| --- | --- | --- |
| GPU memory | 45.6 GB | 39.2 GB |
| Memory bandwidth | 1.55 TB/s | 1.74 TB/s |
| Dense FP8, vendor-listed | 0.64 PFLOPS | 0.98 PFLOPS |
| Dense FP4, vendor-listed | Not supported | 1.96 PFLOPS |

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](https://overgpu.com/docs/nvidia-drivers-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](https://overgpu.com/compare/b200-vs-b300).

## Frequently asked questions

### Is the B200 worth the extra cost over the H200?

It is when you can use what it adds: more than 141 GB per GPU, compute-bound training or busy inference, FP4, or faster NVLink. If your model fits in 141 GB and one H200 keeps up with your traffic, the H200 is the better buy at $1,500 less per month.

### Does the H200 support FP4?

Not in hardware, because FP4 tensor-core math is new with Blackwell. You can still store weights in 4 bits on an H200 with weight-only quantization, but the matrix math runs at higher precision.

### Can I serve a 70B model in BF16 on one GPU?

Not on an H200 in practice, because the weights take about 140 GB of its 141 GB. A B200 holds them with about 40 GB left for the KV cache and runtime, and an FP8 version fits comfortably on either.

### Do I need new software to move from the H200 to the B200?

Usually only newer versions. Blackwell needs CUDA 12.8 or later and framework builds compiled for it, so containers pinned to older releases or custom Hopper kernels may need a rebuild.

### Are both available as 8-GPU servers?

Yes. The HGX H200 costs $23,900 a month and the HGX B200 $34,900, both bare-metal servers with eight GPUs on NVLink and 8× 400G networking, and both are in stock.

## Related pages

- [Rent NVIDIA H200 and HGX H200 servers from $3,090 per month](https://overgpu.com/gpus/nvidia-h200): Rent a dedicated NVIDIA H200 (141 GB) for $3,090/month, about $4.23 per GPU-hour at 24/7, or an 8-GPU HGX H200 for $23,900. Flat price, paid in crypto.
- [Rent NVIDIA B200 and HGX B200 servers from $4,590 per month](https://overgpu.com/gpus/nvidia-b200): $4,590/month for a dedicated 180 GB B200 (about $6.29 per GPU-hour at 24/7) or $34,900 for an HGX B200 server. Delivered in under 10 minutes.
- [H100 vs H200: same Hopper compute, very different memory](https://overgpu.com/compare/h100-vs-h200): Same Hopper compute, 80 GB at 3.35 TB/s vs 141 GB at 4.8 TB/s. What fits on one GPU, the KV cache math and the monthly cost, compared.
- [B200 vs B300: is Blackwell Ultra worth the upgrade?](https://overgpu.com/compare/b200-vs-b300): 180 vs 288 GB of HBM3e, the FP4 uplift and 400G vs 800G networking. See what fits, the cost per GB and when the B200 is enough.
- [MI355X vs B200: memory, software and cost compared](https://overgpu.com/compare/mi355x-vs-b200): 288 GB vs 180 GB of HBM, ROCm vs CUDA, 4-bit formats and monthly cost per GPU. See which 8-GPU server fits your models and budget.
- [GPU servers for LLM training on dedicated 8-GPU nodes](https://overgpu.com/solutions/llm-training): Bare-metal HGX B300, B200 and H200 nodes with NVLink and 400–800G networking, rented monthly. See the memory math first.