The H100 vs B200 decision comes down to model size and budget. B200 delivers up to 4x the inference throughput of H100 for AI workloads, but H100 wins on cost per token for models under 70B parameters.
The H100 vs B200 decision comes down to one number: 80 GB versus 192 GB. If your model fits in 80 GB, H100 is the better value. If it does not, B200 is the only single-GPU option.
Key takeaways
The H100 vs B200 decision is not about which GPU is faster. B200 is faster on every benchmark. The real question is whether that performance gap translates into lower cost-per-token for your specific workload. For models under 34B parameters that fit inside H100's 80 GB, the answer is usually no. For 70B+ models or workloads that need FP4 precision, the answer is usually yes.
Raw TFLOPS do not directly translate to cost-per-token. What matters is how much of the GPU's capacity your workload actually uses, and whether the memory advantage of the B200 changes the number of GPUs you need.
For models that fit inside 80 GB at inference precision (roughly sub-34B at FP16, or sub-70B at FP8), a single H100 handles the workload. Adding B200's extra memory does not reduce GPU count. The H100 at $2.50/hr delivers adequate throughput at a 33% lower hourly cost than B200 at $3.75/hr. For teams serving Llama 3.1 8B or 13B at scale, H100 is the more cost-efficient choice.
Llama 3.3 70B at FP16 requires approximately 140 GB of VRAM. H100 SXM has 80 GB, so you need two H100s to serve it without quantization: $5.00/hr for two H100s versus $3.75/hr for one B200 that fits the model natively. B200 is 25% cheaper and requires no tensor parallelism overhead or NVLink multi-GPU coordination for 70B serving.
At FP8 (70 GB), the 70B model fits on a single H100 with 10 GB headroom for KV cache. This is viable but tight, and KV cache pressure limits batch size. The B200 at FP8 leaves 122 GB free for KV cache, which directly increases maximum throughput per GPU.
H100 has no hardware FP4 support. If your serving stack runs FP4 inference (vLLM with FP4 quantization, or TensorRT-LLM with FP4 mode), only B200 can execute it natively. At FP4, a 70B model occupies approximately 35 GB, leaving 157 GB of KV cache headroom on a single B200. This is the highest throughput configuration available on any single GPU in 2026.
LLM token generation is memory-bandwidth-bound, not compute-bound. Each forward pass reads all model weights from GPU memory. The faster the memory bandwidth, the more tokens per second the GPU generates at a given batch size.
B200 delivers 8 TB/s, 2.4× the H100's 3.35 TB/s. In practice, this translates to roughly 2Ó higher tokens-per-second on memory-bandwidth-limited workloads (small batch inference where the GPU is not compute-saturated). For large-batch inference where the GPU is compute-saturated, the B200's FP8 TFLOPS advantage (2.3×) also compounds.
B200 SXM is available on-demand on packet.ai today: $3.75/hr Dynamic (scheduler-isolated) and $5.90/hr Dedicated (single-tenant, 99.99% SLA). SSH-ready in under 5 minutes.
H100 SXM is on the waitlist at $2.50/hr. Join the H100 waitlist here.
For teams that need a GPU now and are working with models under 80 GB, the A100 80GB at $1.43/hr is live and handles sub-34B models at FP16 and 70B at FP8 with tight headroom.
Teams today can browse available cluster configurations on packet.ai.
Last reviewed: July 2026. Deploy a B200 on packet.ai from $3.75/hr, or join the H100 waitlist. Browse available GPU clusters on packet.ai.
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