A16

The A16 is a Ampere-generation NVIDIA GPU built on the GA102 architecture, manufactured on a Samsung 8nm process node with 28.3 billion transistors. Released in 2021, it delivers 62.4 TFLOPS of FP16 throughput and 62.4 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GA102 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 250W TDP, the A16 sits in the moderate power envelope (250W), suitable for both workstation and data center deployments.

Memory capacity is 64GB of GDDR6 with 432 GB/s bandwidth. This determines which models can run without quantization: approximately 32B parameters at FP16 (2 bytes/param), 64B at INT8 (1 byte/param), or up to 128B 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 144 FLOP/byte (62.4 TFLOPS ÷ 432 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 432 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The A16 uses PCIe 4.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 64GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 64GB, the alternative is pipeline parallelism (splitting model layers across cards) rather than tensor parallelism, which introduces inter-card communication overhead at each layer boundary. This makes the A16 best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the A16 are VDI / virtual workstations, Multi-tenant inference, High-density deployments. Quad-GPU card with 4×16GB GDDR6 (64GB total). Designed for VDI and inference. Low per-GPU cost. Key limitations to factor into your evaluation: Very low memory bandwidth per GPU (108 GB/s per die); No NVLink — cannot pool VRAM across cards; Low FP16 throughput — not suited for large model training. 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 A16 is a 5-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 A16 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.