H100 vs H200 vs B200: What's the Difference

"Is H100 enough for our model, or do we need H200? Should we go all the way to B200?" It's the question teams hit most when adopting GPUs. Here's what actually separates the three, and which fits which workload.

The big picture: different architectures

The three split across two architecture generations.

H100 and H200 share the same Hopper architecture. The compute die itself is identical; the decisive difference is memory. Think of H200 as an H100 with its memory swapped for larger, faster HBM3e.

B200 is the next-gen Blackwell architecture. The compute structure is newly designed, and notably it natively supports low-precision inference math (FP4) — a major change.

Core spec comparison

Per 2026 public datasheets:

SXM form factor, per 2026 public specs; varies by configuration.

H100 → H200: why does only-a-memory-change make it faster?

H100 and H200 share compute cores. Yet H200 is noticeably faster on memory-bound work, because bandwidth rose from 3.35 to 4.8TB/s — about 43%.

As covered before, LLM inference is memory-bound. Each token requires reading weights from memory, and that read speed sets generation speed. So even with identical compute, faster memory raises inference throughput. And with capacity up from 80GB to 141GB, you can fit larger models on one card or hold more KV cache for long contexts.

H200 → B200: a generational shift

B200 isn't just a memory upgrade — it's a generational change. Memory bandwidth jumps again to ~8TB/s, and capacity rises to 192GB.

The most important change is native FP4 support. FP4 is 4-bit low-precision math that lets the same hardware push far more inference throughput. NVIDIA cites B200 as several times faster than H100 on LLM inference, and this FP4 support is one core reason.

So which should you choose?

It depends on the workload.

The three deciding factors: model size (must it fit on one card?), workload character (memory-bound inference or compute-bound training?), and budget.

In reality, though, you can't decide on the spec sheet alone. Even with the same B200, real throughput varies widely by model structure, parallelization, and batch size. "Rated performance" and "performance on your workload" are different things.

FAQ

Q. Should I buy H100 or H200?
If you must fit a large model on one card or need long context / high inference throughput, H200 wins. Otherwise H100 offers better value.

Q. Is B200 better for training or inference?
Both improve, but native FP4 gives it an especially large edge on large-scale inference.

Q. Is H200 always faster than H100?
Definitely on memory-bound work (most LLM inference). On compute-bound work, identical cores can mean a small gap.

Q. Can I pick a GPU on specs alone?
Specs are only a starting point. Benchmark on your real workload to know true throughput and cost.

Conclusion: spec comparison starts it, measurement decides it

H100, H200, and B200 differ clearly in memory and architecture, and the best pick shifts by workload. But choose on rated specs alone and you may not get that performance on your actual model.

TEN's assessment service RA:X benchmarks GPU performance against real AI workloads. Instead of comparing rated specs, it measures actual throughput and communication patterns by model architecture, data size, and parallelization — so you can decide the optimal GPU configuration from measured data. GPUs you adopt are then partitioned and allocated with AIPub, keeping expensive latest-gen GPUs from sitting idle.

To validate the right GPU configuration for your workload by measurement, get a RA:X assessment.

References

• NVIDIA, H100 / H200 / B200 Product Datasheet (2026)

• NVIDIA, Blackwell Architecture Technical Brief

• NVIDIA, Transformer Engine and FP4 Documentation