How much faster is the NVIDIA B200 for LLM inference compared to H100 and H200, and is the speedup worth the price?

Teams evaluating B200 usually want a single number: how many times faster is it than H100 and H200, and does that justify the higher rate. The honest answer resists a single number, because the speedup depends on the model, precision, and batch size, and "worth it" is a cost question, not a speed one. B200 is faster than H100 and H200 for LLM inference, but the exact multiple varies by workload, and whether it is worth the price depends on cost per token, not raw throughput, since a faster chip at a higher rate can still be cheaper or more expensive per token. This guide separates the speed question from the value question and shows how to answer both for your own model.

Speed comes from memory and architecture, not one benchmark

B200 is the newer Blackwell-generation chip, and its advantage over H100 and H200 comes mostly from higher memory bandwidth, larger high-bandwidth memory, and newer precision formats. Those are the levers that lift tokens per second, but how much they help is workload-specific.

The generational order is clear even when the exact multiple is not. B200 generally delivers higher LLM inference throughput than H200, which in turn exceeds H100, driven by memory bandwidth and Blackwell-era precision support, but published speedup multiples are stated for specific configurations and should be treated as directional until measured on your model. A headline "several times faster" figure almost always assumes a particular model, precision, and batch size. Change any of those and the multiple moves, sometimes a lot, which is why the only speedup number you should budget against is the one you measure.

What decides how much faster B200 is for your model

Two teams can benchmark the same three chips and report different B200 advantages. Four factors explain the spread:

• Model size and memory fit: Large models that batch poorly or spill memory on H100 see the biggest B200 gains. Models that already fit comfortably see smaller ones.
• Precision format: Newer Blackwell precision formats can lift throughput well beyond the raw hardware ratio, but only if your serving stack actually uses them.
• Batch size and concurrency: B200's advantage compounds at high concurrency. A lightly loaded endpoint may show little difference over H200.
• Sequence length: Long-context inference stresses memory bandwidth, favoring the newer chip; short prompts narrow the gap.

H200 sits between H100 and B200 for a specific reason: it shares H100's Hopper architecture but adds substantially more memory bandwidth and capacity, so it already closes part of the gap on memory-bound inference. That means B200's advantage over H200 is often smaller than its advantage over H100, which matters when you decide whether to jump one generation or two.

Fast enough is a cost-per-token question

Raw speed is only half the decision. The other half is whether the speedup pays for the higher rate, and that is a per-token comparison.

Comparison	What to measure	Worth-it test
B200 vs H100	Tokens/hour on your model	B200 wins per token above ~2x H100 throughput
B200 vs H200	Tokens/hour on your model	B200 wins per token above its rate ratio to H200
Speed alone	Latency at target concurrency	Only matters if latency is the binding constraint

The rule generalizes: divide each chip's hourly rate by its measured tokens per hour, and the lower cost per token wins unless latency is your hard constraint. In our current published listings, H100 is from $2.00, H200 from $2.60, and B200 from $4.00 per GPU-hour under Limited Availability, so B200 has to clear roughly 2x H100's throughput, and a smaller margin over H200, to be worth it on cost. If latency is the constraint rather than cost, the calculus flips and raw speed can justify the premium on its own.

Benchmarking B200, H200, and H100 on GMI

Once you accept that both questions need your own numbers, the practical step is using our platform, which carries all three chips so you can measure them under one billing model. We are an AI-native GPU and inference cloud that publishes dedicated NVIDIA GPU list pricing and lists H100, H200, and B200, which lets you benchmark speed and compute cost per token directly.

We currently list H100 at from $2.00, H200 at from $2.60, and B200 at from $4.00 per GPU-hour, so you can run the same model on each and settle both the speedup and the cost-per-token question with measured data rather than a headline multiple. Verify current rates and B200's Limited Availability status on our GPU infrastructure (https://www.gmicloud.ai/en/gpus) and pricing (https://www.gmicloud.ai/en/pricing), since Blackwell pricing and stock move quickly. Benchmark at the precision, batch size, and sequence length you will run in production, because those inputs decide the real multiple. When the workload is sustained production serving, our Prime Inference (https://www.gmicloud.ai/en/models/prime-inference) provides reserved GPU capacity with single-tenant isolation and warm, weights-preloaded serving, so your measured throughput reflects steady traffic rather than cold-start overhead. Start a comparison run in our console (https://console.gmicloud.ai).

Measure the speedup, then price it

If you pick B200 on a headline speed multiple, you risk paying a premium the workload does not repay, and if you rule it out on the hourly rate alone, you may miss real savings on a memory-bound model. Answer both questions with your own data: measure tokens per hour for your model on H100, H200, and B200, then divide each rate by that number to get cost per token. B200 is faster, but whether it is worth it turns on whether the speedup clears the rate premium, and only your workload on the invoice math can tell you that.