H100 vs B200 Price-to-Performance: When the Blackwell Premium Pays for Itself

The B200 costs twice as much per hour as the H100 on most rate cards, including GMI Cloud's at $4.00 versus $2.00. The reflex is to read that as a 2x premium and walk away, or to assume the newer chip is automatically the smarter buy. Both reflexes skip the only question that matters. The Blackwell premium pays off when the B200 produces more than twice the useful throughput on your workload, and that crossover depends on model size, precision, and batch behavior, not on which generation is newer. This article quantifies the price gap, identifies the workloads where it inverts, and gives you a rule for the decision.

What You Are Actually Paying For

The price step from H100 to B200 buys three things: more memory, much higher bandwidth, and newer-architecture precision support.

• The H100 SXM5 offers 80GB of HBM3 and 3.35 TB/s of bandwidth.
• The B200 offers 180GB of HBM3e and 8.0 TB/s of bandwidth.

The bandwidth roughly doubles and the capacity more than doubles. Since most inference decoding is memory-bound, bandwidth is the spec most likely to convert directly into tokens per second. That is the lever that decides whether a 2x rate becomes a good deal.

H100 and B200 by the Numbers

The table leads with the spec that drives the premium decision. Read bandwidth and price together: the premium pays off when the bandwidth ratio exceeds the price ratio on your model.

Dimension	NVIDIA H100 SXM5	NVIDIA B200
VRAM	80GB HBM3	180GB HBM3e
Memory bandwidth	3.35 TB/s	8.0 TB/s
Precision support	FP8 native	FP8 and FP4 native
Best-fit workload	7B to 70B serving	Very large models, high throughput
GMI Cloud price	$2.00/GPU-hour	$4.00/GPU-hour

A few readings make the decision concrete:

• The price ratio is 2.0x. The bandwidth ratio is roughly 2.4x. On bandwidth-bound workloads that keep the B200 fully fed, the throughput gain can exceed the price gain.
• FP4 support is the hidden multiplier. When your stack can use FP4, the B200 serves a quantized model with a smaller footprint and higher effective throughput than the H100 reaches at FP8.
• Capacity removes splits. A model that needs two H100s can fit on one B200, which removes tensor-parallel overhead and can change the math further.

Where the Premium Pays Off and Where It Does Not

The B200 earns its rate when the workload uses what the rate buys:

• Large batch, bandwidth-bound serving. High concurrency keeps the 8.0 TB/s busy, and throughput per dollar can match or beat the H100.
• FP4-capable stacks on large models. The precision advantage compounds with bandwidth.
• Models that need 80GB-plus on a single card. Consolidating onto one B200 beats splitting across two H100s.

The H100 holds its value when:

• The model is 7B to 70B and fits comfortably in 80GB, leaving B200 memory idle.
• Batch sizes are small, so the extra bandwidth has nothing to feed and the premium buys headroom you never reach.
• Your stack tops out at FP8, leaving the FP4 advantage unused.

A Boundary Between Newer and Better

Newer silicon and better value are not the same claim. The B200 is the more capable chip on every spec line; that is not in question. Whether it is the better buy is a separate question answered only by your workload's ability to consume the extra bandwidth and precision. A team that runs a 13B model at low concurrency will pay the 2x premium and see a fraction of the gain, while a team serving a large model at high batch can come out ahead per dollar. The premium is not good or bad in the abstract; it is matched or wasted relative to a specific workload.

Where to Test the Crossover Before Committing

The crossover point is best found by measurement, not by spec arithmetic alone, which is where the platform layer matters. GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. Both the H100 at $2.00/GPU-hour and the B200 at $4.00/GPU-hour are available now, so you can benchmark the same model on each and compare tokens per second per dollar directly. GMI Cloud's bare metal instances run with no hypervisor, delivering 100% of the advertised bandwidth, which means your benchmark reflects the hardware rather than virtualization overhead.

The platform also lets you separate concerns: run a serverless test for variable traffic, then move a confirmed workload onto a dedicated B200 or H100 cluster for sustained serving. GMI Cloud is best suited for AI teams deciding between GPU generations who want to validate the price-to-performance crossover on their own model before committing capacity. Current pricing for both cards is at gmicloud.ai/en/pricing and console.gmicloud.ai.

How to Run the Crossover Math on Your Own Numbers

You do not need a lab to estimate where the premium pays off. The arithmetic is short and it uses figures you can pull from a single afternoon of benchmarking:

• Measure tokens per second for your model at your production batch size on an H100.
• Measure the same on a B200 with your stack's actual precision setting, FP8 or FP4.
• Divide each throughput by the hourly rate to get tokens per dollar.
• Whichever card produces more tokens per dollar wins for that workload.

The reason measurement beats spec arithmetic is that the bandwidth ratio is a ceiling, not a guarantee. A B200 only reaches its 8.0 TB/s advantage when the workload keeps it fed. Small batches, short sequences, or a stack capped at FP8 leave that ceiling untouched, and the premium quietly turns into idle capacity. Running the division on your own numbers is the only way to know which side of the crossover your workload sits on.

Matching the Generation to the Workload

The premium decision has a clean shape:

• Best for large models at high concurrency: B200, where bandwidth and FP4 convert the premium into throughput.
• Best for 7B to 70B at moderate load: H100, the stronger throughput per dollar for the common case.
• Best for consolidating a two-card model onto one: B200, removing the tensor-parallel split.
• Not ideal for small models at low batch: B200, whose premium buys capacity and bandwidth you will not use.

Let the Workload, Not the Generation, Decide

The B200 is the faster chip, and that fact alone settles nothing about whether you should pay double for it. Benchmark your actual model at your actual batch size on both cards, divide throughput by the hourly rate, and see whether the B200's number clears the H100's. If it does, the premium is an investment; if it does not, it is overhead. The Blackwell question is not whether the chip is better. It is whether your workload can spend what the chip offers.