Specifications: P100 16GB vs V100 32GB
The P100 16GB and V100 32GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 16GB to 32GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The V100 32GB 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 V100 generally achieves 30-120% higher throughput than the P100 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the P100 serves 1,800-2,400 tok/s while the V100 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the P100 while the V100 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the P100 versus 18-35ms for the V100.
Training Throughput Comparison
On training workloads, the V100 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 P100 versus 5,000-8,000 tok/s on the V100. Model FLOPS utilization (MFU) ranges from 38-48% on the P100 and 42-55% on the V100. Memory capacity constraints on the P100 require activation checkpointing for models larger than 13B, while the V100 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The P100 16GB has 16GB VRAM, while the V100 32GB has 32GB 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 P100 averages $0.30/hr while the V100 averages $0.60/hr. However, cost-per-token analysis often favors the V100 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 P100 cluster costs $445K-$533K while the V100 cluster costs $699K-$1390K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the P100 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 V100 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.