Hopper vs Blackwell: NVIDIA’s GPU generations compared

If you are choosing GPUs for an AI workload right now, you are really choosing between two NVIDIA generations. Hopper is the H100 and H200. Blackwell is the B200 and B300, and the GB200 and GB300 systems built around them. They overlap heavily in what they can do, so the useful question is almost never "which is better" in the abstract. It is which one fits the job in front of you, and how much you are willing to pay for headroom you might not use.

We rent both, so we have this conversation most weeks. Here is how the two generations actually differ, and how we help teams land on one.

The short version

Hopper launched in 2022 and trained most of the models you have heard of. It is mature, widely available, and cheaper per GPU-hour. Blackwell shipped in volume through 2024 and 2025: a physically bigger chip, with far more memory, a new low-precision format aimed at inference, and roughly double the interconnect bandwidth. For the largest models and the heaviest training runs, Blackwell is a genuine step up. For a lot of everyday training, fine-tuning and inference, Hopper still does the job, and your budget stretches further.

Memory is the difference you feel first

Compute numbers make for good slides, but the thing that usually decides whether a GPU works for you is memory: how much, and how fast. This is where the two generations separate most clearly.

• H100 (Hopper): 80 GB of HBM3 per GPU, around 3.4 TB/s of bandwidth.
• H200 (Hopper, refreshed): 141 GB of HBM3e at roughly 4.8 TB/s. The same compute as the H100, but much more room and bandwidth, which tells on long context and larger models.
• B200 (Blackwell): 192 GB of HBM3e, in the region of 8 TB/s.
• B300 (Blackwell Ultra): 288 GB of HBM3e, the most of the four.

What that buys you in practice: a model that would need sharding across several H100s to fit can often sit on a single B200 or B300. Fewer GPUs for the same model means less communication overhead and usually a simpler, cheaper deployment. If you are unsure how much you need, we worked through the arithmetic in our guide to GPU memory.

What actually changed in the architecture

Blackwell is more than a quicker Hopper. The biggest change is physical. Where a Hopper GPU is a single die, each Blackwell GPU is two large dies joined by a very fast link and presented to your software as one device, which is how NVIDIA got past the size limit of a single chip. Layered on that is a second-generation Transformer Engine with 4-bit floating point (FP4), aimed squarely at inference, where on the right workloads it can roughly double throughput. Training improves as well, though the exact multiple swings a lot with the model and the precision you run.

Hopper is where the Transformer Engine and FP8 arrived, and FP8 is still the workhorse precision for a great deal of training. If your pipeline is already built and tuned around FP8 on H100s, moving up to Blackwell is an upgrade rather than a rewrite.

The interconnect, and why it matters at scale

Inside a single node of eight GPUs, both generations use NVLink and NVSwitch so the GPUs talk to each other far faster than they could over the network. Blackwell's fifth-generation NVLink roughly doubles Hopper's bandwidth, to about 1.8 TB/s per GPU. On one node you may never notice. It starts to matter once you go multi-node and the GPUs spend real time swapping gradients, which is the single most underrated factor in whether a cluster scales. We dug into it in the fabric that makes eight GPUs act as one.

Blackwell also anchors NVIDIA's rack-scale systems, the GB200 and GB300 NVL72, which wire 72 GPUs together over NVLink as though they were one enormous accelerator. That is a different class of deployment from a single bare-metal node, and we cover what to check before committing to one in renting a multi-node B300 cluster.

Power, availability and price

Bigger chips pull more power. A Hopper H100 node runs happily in most datacentres, while Blackwell nodes run hotter and often want liquid cooling, which limits where they can live and lifts the cost to operate them. All of that shows up in the price. Hopper is cheaper per GPU-hour and, two years into its life, far easier to get. Blackwell carries a premium, and for the newest parts you are frequently waiting on allocation.

On our own rates the gap is concrete: H100 nodes start at $2.95 per GPU-hour, B200 at $3.75, and B300 at $4.50, all on our longest terms. Whether the step up earns its keep depends entirely on the workload, which is the decision that actually matters.

So which should you use?

A few rules of thumb that hold up in the field:

• Stay on Hopper (H100 or H200) when your model and batch fit its memory, your stack is already tuned for it, and cost per useful result matters more than the last increment of speed. That describes a lot of production inference and a lot of fine-tuning.
• Move up to Blackwell (B200 or B300) when you are memory-bound, when you are training or serving very large models or long context, when FP4 inference throughput changes your unit economics, or when avoiding multi-node sharding on Hopper would cost you more than one bigger node.
• Between the two Blackwell parts, the B200 is the value choice and the B300 adds memory and headroom for the largest jobs. We put them head to head in B300 vs B200.

The mistake we see most is buying the newest, biggest GPU out of habit when a smaller or older one would have finished the job for less. The second most common is the opposite: forcing a model that does not fit onto cheaper GPUs, then handing the saving back in sharding complexity and slower runs. More often than not the right call is the cheapest setup that comfortably fits the workload, not the fastest chip on the page.

Renting either, with us

We deliver all of these as dedicated bare-metal nodes with root access, in India, the EU and the US: the H100 for proven, cost-effective Hopper compute, and the B200 and B300 for Blackwell. You run your own stack and we keep the hardware healthy for the term. If you are still weighing renting against buying or using an API, this guide lays out the trade-offs, and what a run costs shows the sums with real numbers.

Not sure which generation fits? Tell us the model, the method and the deadline, and we will give you a straight answer on which GPU is the lower total cost for your workload. Current rates are on the pricing page, or talk to an engineer.