Specifications: B200 192GB vs MI350X 288GB
The B200 192GB and MI350X 288GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 192GB to 288GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The MI350X 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 MI350X generally achieves 30-120% higher throughput than the B200 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the B200 serves 1,800-2,400 tok/s while the MI350X reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the B200 while the MI350X may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the B200 versus 18-35ms for the MI350X.
Training Throughput Comparison
On training workloads, the MI350X 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 B200 versus 5,000-8,000 tok/s on the MI350X. Model FLOPS utilization (MFU) ranges from 38-48% on the B200 and 42-55% on the MI350X. Memory capacity constraints on the B200 require activation checkpointing for models larger than 13B, while the MI350X accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The B200 192GB has 192GB VRAM, while the MI350X 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 B200 averages $4.00/hr while the MI350X averages $3.00/hr. However, cost-per-token analysis often favors the MI350X 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 B200 cluster costs $406K-$1005K while the MI350X cluster costs $1214K-$1291K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the B200 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 MI350X 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.