H100 vs B200
Inference Economics
B200 is 2.4× faster than H100 but costs 2.6–3.2× more. The economics only work in B200's favor when your model requires multiple H100s — here's exactly where the crossover happens.
TL;DR
For models up to 70B FP8 (single H100 fit): H100 is cheaper per token. For 70B FP16 and larger: B200 wins — its 192GB VRAM avoids tensor parallelism overhead. The bigger your model, the more B200 outperforms.
Specifications Compared
Architecture
Hopper (SXM5)
Blackwell SXM
VRAM
80GB HBM3
192GB HBM3e
Memory Bandwidth
3.35 TB/s
8.0 TB/s
FP8 TFLOPS (dense)
1,979
4,500
FP16 TFLOPS (dense)
989
2,250
TDP
700W
1,000W
NVLink Bandwidth
900 GB/s
1,800 GB/s
On-Demand (Lambda/CoreWeave)
~$2.49–2.69/hr
~$6.99–8.00/hr
Price Ratio vs H100
1×
2.6–3.2×
Memory BW Ratio vs H100
1×
2.4×
Cost per Million Tokens by Model
Lambda Labs pricing. H100 $2.49/hr, B200 $6.99/hr. vLLM throughput at batch 16.
| Model | Quant | H100 Config | H100 $/1M | B200 Config | B200 $/1M | Winner |
|---|---|---|---|---|---|---|
| Llama 3.1 8B | FP16 | 1× H100 | $0.13 | 1× B200 | $0.16 | H100 ✓ |
| Llama 3.1 70B | FP8 | 1× H100 | $0.38 | 1× B200 | $0.43 | H100 ✓ |
| Llama 3.1 70B | FP16 | 2× H100 | $0.63 | 1× B200 | $0.46 | B200 ✓ |
| Llama 3.1 405B | FP8 | 8× H100 | $5.53 | 3× B200 | $1.82 | B200 ✓ |
| DeepSeek R1 671B | FP8 | 8× H100 INT4 | $7.90 | 4× B200 FP8 | $2.59 | B200 ✓ |
| Mistral 7B | FP16 | 1× H100 | $0.12 | 1× B200 | $0.15 | H100 ✓ |
Why B200 Wins on Large Models
The VRAM Parallelism Problem
H100's 80GB forces tensor parallelism for 70B+ FP16 models. Each additional GPU adds ~10–15% synchronization overhead. Two H100s don't deliver 2× throughput — more like 1.7×. B200's 192GB eliminates this.
Memory Bandwidth Scales Better
LLM inference is memory-bandwidth-bound. B200's 8.0 TB/s is 2.4× H100's 3.35 TB/s. At the same model size, B200 moves weights to compute 2.4× faster — directly translating to 2.4× more tokens per second.
The 405B/671B Sweet Spot
For DeepSeek R1 (671B), B200 needs 4× GPUs (768GB FP8) vs H100 needs 8× (640GB INT4). Same model, half the nodes, 3× the throughput. B200 delivers $2.60/M tokens vs H100's $7.90/M — a 3× improvement.
Decision Guide
Choose H100 if:
- →Serving models ≤ 70B FP8 (fits in 80GB with quantization)
- →Inference cost is primary concern and model fits in 80GB
- →Budget is constrained or B200 unavailable
- →Team needs maximum GPU availability/options
Choose B200 if:
- →Serving 70B FP16 or larger models (405B, 671B)
- →Maximum throughput is required for SLA compliance
- →You can accept 3–4 month wait for availability
- →Running a commercial inference service where latency = revenue
FAQs
Is B200 better than H100 for LLM inference?
It depends on model size. For small models (7B–70B) that fit on a single H100 (80GB) with FP8 quantization, H100 is more cost-effective per token because it's 2.6–3× cheaper per hour. For large models (70B+ FP16, 405B, DeepSeek R1 671B) that need multiple H100s, B200's 192GB VRAM can serve the model on fewer GPUs — resulting in better cost per token despite the higher hourly rate.
At what model size does B200 beat H100 on cost per token?
B200 wins at cost per token when models require 2+ H100s for serving. The breakeven is roughly 70B FP16 (needs 2× H100 at $4.98/hr, or 1× B200 at $6.99/hr — B200 is cheaper per token due to lower parallelism overhead). For 405B, B200 wins decisively: 8× H100 at $19.92/hr vs 3× B200 at ~$21/hr, with B200 delivering 3× more throughput.
How much faster is B200 than H100 for inference?
B200 has 2.4× the memory bandwidth of H100 (8.0 TB/s vs 3.35 TB/s), which translates to approximately 2–3× more tokens/second for memory-bandwidth-limited LLM inference. For compute-bound scenarios (short context, small batches), the 2.3× TFLOPS advantage also applies. Real-world inference speedups range from 1.8× to 3× depending on model size, context length, and batch size.
What is the B200 cloud price?
NVIDIA B200 cloud pricing as of May 2026: CoreWeave offers B200 SXM at approximately $6.99–8.00/hr per GPU. AWS, GCP, and Azure are making B200 available in limited regions at higher prices. B200 is still in limited availability — CoreWeave and selected specialist clouds have the broadest access. Lambda Labs has B200 on a waitlist.