Specifications: H200 141GB vs B300 288GB
The H200 141GB and B300 288GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 141GB to 288GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The B300 288GB supports newer technologies like FP8 transformer engines, fourth-gen tensor cores, and higher-bandwidth HBM3e or HBM4 memory.
AI Inference Performance
For LLM inference, the B300 generally achieves 30-120% higher throughput than the H200 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the H200 serves 1,800-2,400 tok/s while the B300 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the H200 while the B300 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the H200 versus 18-35ms for the B300.
Training Throughput Comparison
On training workloads, the B300 delivers 40-120% higher throughput for typical model sizes. For a 7B model at BF16 mixed precision, per-GPU throughput reaches 3,500-4,500 tok/s on the H200 versus 5,000-8,000 tok/s on the B300. Model FLOPS utilization (MFU) ranges from 38-48% on the H200 and 42-55% on the B300. Memory capacity constraints on the H200 require activation checkpointing for models larger than 13B, while the B300 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The H200 141GB has 141GB VRAM, while the B300 288GB has 288GB VRAM. At FP16, a 7B model requires ~14 GB for weights, plus KV cache of ~1.5 GB per 128K context per request. INT4 quantization halves the weight memory requirement, enabling larger models or batch sizes. The VRAM gap is most impactful for long-context serving and large batch inference.
Cloud Pricing and TCO
On-demand cloud pricing for the H200 averages $2.80/hr while the B300 averages $5.50/hr. However, cost-per-token analysis often favors the B300 by 15-40% for sustained production workloads due to higher throughput. Reserved 12-month contracts provide 30-50% discounts. For a 64-GPU, 3-year TCO, the H200 cluster costs $879K-$736K while the B300 cluster costs $683K-$806K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the H200 for: budget-constrained deployments, models under 13B that fit in available VRAM, batch inference workloads where throughput per dollar is secondary to absolute cost, and development/staging environments. Choose the B300 for: production serving at scale, models larger than 13B parameters, workloads requiring FP8/FP4 precision, long-context inference beyond 128K tokens, and clusters above 128 GPUs where scaling efficiency matters.