Foreword
In my last article, What Can You Do with Rented GPU Compute?, I gave each card a one-line positioning. Today I want to walk through the differences between the H200 and the B200 in detail. If you are weighing up LLM inference, I hope this helps you decide and saves you some cost. This article covers specs, throughput, ecosystem, and power, and ends with a handy decision checklist.
What Actually Differs on the Spec Sheet
Let us get the hardware facts straight first, because the marketing numbers can be confusing.
Memory: H200 has 141GB of HBM3e. B200 has 192GB. That is a 36% jump, and for inference, memory decides what model fits on one card.
Memory bandwidth: H200 runs at 4.8TB/s. B200 pushes around 8TB/s. Since inference is largely memory-bound, this directly shapes token throughput.
Interconnect: H200 NVLink tops out at 900GB/s. B200 doubles it to 1.8TB/s. This matters when you shard a large model across multiple GPUs.
Compute formats: B200’s fifth-generation Tensor Cores add FP4 support, on top of the FP8 that H200 already handles. Quantized inference is where B200 pulls far ahead.
One more thing worth knowing: B200 is effectively two dies in one package. Most software sees it as a single GPU, but it explains both the performance jump and the power jump.
Throughput in Real Inference
Spec sheets sell potential; inference serves tokens per second. In practice, three factors decide what you get:
Batch size first. With small batches, both cards are memory-bound, so the real-world gap is much smaller than the paper gap. With large batches, B200’s extra bandwidth and FP4 compute start to show, and throughput can pull clearly ahead of H200.
Quantization second. If your stack supports FP8, H200 is already efficient. If you can move to FP4 on B200, you cut both compute time and effective memory footprint. That is a double win for high-volume serving.
Model size third. A 70B model in FP8 needs roughly 70GB of weights. H200 holds it on a single card with headroom for KV cache. Larger models or heavier cache pressure push you toward B200’s 192GB, or toward multi-GPU sharding.
The honest summary: for small-batch, low-volume inference, H200 delivers most of the experience at a lower rental price. For large-batch, high-volume serving, B200’s advantage becomes real money.
Ecosystem Maturity: The Part Nobody Puts on the Slide
Here is the part I learned the hard way: newer silicon means newer software requirements.
H200 rides on the Hopper stack that has been battle-tested for years. vLLM, TensorRT-LLM, and most inference frameworks run on it without surprises.
B200 requires a newer CUDA and driver baseline. The mainstream frameworks do support it now, but you should expect an occasional version pin, a beta kernel, or a workaround that your H200 neighbor never thinks about. If your team wants “upgrade and forget,” H200 is the calmer choice. If you enjoy living on current-gen stacks, B200 will not scare you.
Power and Availability
Two practical constraints that rarely make it into comparisons:
Power. H200 cards run up to around 700W. B200 pushes past 1000W per GPU. When you rent whole servers, that difference shows up in cooling requirements and sometimes in what your provider can even offer.
Availability. H200 supply is mature and rental inventory is deep. B200 inventory is growing fast in 2026, but peak-demand slots still book out earlier. If you need guaranteed capacity next week, check availability before you check the benchmark charts.
Which One for Your Workload
The checklist I actually use:
7B–70B models, moderate traffic, tight budget → H200. Single-card fit for 70B FP8, mature stack, cheaper hourly rate.
70B+ models or heavy KV cache → B200. The 192GB and 8TB/s buy you fewer sharding headaches.
High-volume serving with FP4 quantization → B200. This is the workload it was built for.
Prototype and validate, then scale → Rent H200 to prove the pipeline, move to B200 when traffic justifies it.
Long context, large batch, latency-critical APIs → B200, and consider multi-GPU from day one.
If you are comparing live options, VMRack’s GPU line covers H100, H200, B200 and the newer Blackwell flagships, so the single-card vs multi-card path is easy to test with short rentals before committing.
Final Thoughts
The H200 vs B200 decision is not “old vs new.” It is a workload question. H200: 141GB, 4.8TB/s, a calm and mature stack, and the best price-to-fit for most mid-size models. B200: 192GB, 8TB/s, FP4, built for high-volume and large-model serving, as long as your team can handle a newer stack.
Match the card to the workload’s memory and traffic shape, not to the benchmark headlines. Rent the smallest card that fits first; you can always scale up.
Thank you very much for reading to the end!
