Tesla P100 16GB

The Tesla P100 16GB is a Pascal-generation NVIDIA GPU built on the GP100 architecture, manufactured on a TSMC 16nm process node with 15.3 billion transistors. Released in 2016, it delivers 18.7 TFLOPS of FP16 throughput and 18.7 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GP100 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 250W TDP, the Tesla P100 16GB sits in the moderate power envelope (250W), suitable for both workstation and data center deployments.

Memory capacity is 16GB of HBM2 with 732 GB/s bandwidth. This determines which models can run without quantization: approximately 8B parameters at FP16 (2 bytes/param), 16B at INT8 (1 byte/param), or up to 32B parameters at INT4/GGUF quantization (0.5 bytes/param). These figures are theoretical maximums — actual capacity is reduced by KV cache, framework overhead, and activation memory, typically by 10–20% for inference and 30–40% for training. The arithmetic intensity ceiling is approximately 26 FLOP/byte (18.7 TFLOPS ÷ 732 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 732 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

Multi-GPU configurations are a first-class use case for the Tesla P100 16GB. NVLink 1.0 / PCIe 3.0 provides 160 GB/s of bidirectional NVLink bandwidth between cards, enabling tensor-parallel inference across multiple GPUs with near-linear VRAM scaling. A two-card configuration provides 32GB of pooled VRAM — sufficient for 16B parameter models at FP16 — while a four-card setup reaches 64GB. NVLink's low-latency, high-bandwidth fabric makes all-reduce operations in data-parallel training significantly faster than PCIe-based alternatives, which top out at ~64 GB/s bidirectional for PCIe 5.0 x16.

The primary workloads for the Tesla P100 16GB are Legacy ML workloads, Budget HPC, Cost-sensitive inference. Legacy Pascal data center GPU. Very low cost per hour. Limited by 16GB HBM2 and older architecture. Still available on some clouds for budget workloads. Key limitations to factor into your evaluation: Legacy Pascal architecture — no Tensor Cores, no FP16 acceleration; Only 16GB HBM2 — limits to small models; Very low throughput vs modern GPUs — 3–5× slower than A100. When comparing this GPU against alternatives at similar price points, the most important metrics are memory bandwidth (for inference throughput), VRAM capacity (for model size), and FP16/BF16 TFLOPS (for training speed). Raw TFLOPS figures can be misleading for inference — a GPU with lower TFLOPS but higher memory bandwidth will often outperform a higher-TFLOPS card on tokens-per-second for memory-bound autoregressive generation.

In the broader GPU market, the Tesla P100 16GB is a 10-year-old design that is increasingly being replaced by newer architectures in cloud deployments, though it remains available at competitive rental rates. CUDA compatibility is a significant advantage: the Tesla P100 16GB benefits from the full NVIDIA software ecosystem including cuDNN, TensorRT, FlashAttention, and all major inference frameworks (vLLM, TGI, TensorRT-LLM). CUDA's maturity means optimized kernels are available for virtually every model architecture. For cloud rental, availability varies significantly by provider — some specialize in this GPU tier while others may have limited stock. Compare on-demand and spot pricing across providers using the rental comparison table on this page, and factor in region availability if latency is a concern for your inference workload.