A100 SXM4 80GB

The A100 SXM4 80GB is a Ampere-generation NVIDIA GPU built on the GA100 architecture, manufactured on a TSMC 7nm process node with 54.2 billion transistors. Released in 2020, it delivers 312 TFLOPS of FP16 throughput and 312 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The GA100 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 400W TDP, the A100 SXM4 80GB sits in the mid-range data center tier (400W), fitting standard GPU server form factors.

Memory capacity is 80GB of HBM2e with 2000 GB/s bandwidth. This determines which models can run without quantization: approximately 40B parameters at FP16 (2 bytes/param), 80B at INT8 (1 byte/param), or up to 160B 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 156 FLOP/byte (312 TFLOPS ÷ 2000 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 2000 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 A100 SXM4 80GB. NVLink 3.0 / SXM4 provides 600 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 160GB of pooled VRAM — sufficient for 80B parameter models at FP16 — while a four-card setup reaches 320GB. 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 A100 SXM4 80GB are ML training, Large model inference, HPC workloads. SXM4 form factor A100 with maximum NVLink bandwidth. The workhorse of the AI era. Widely available with mature software support. Key limitations to factor into your evaluation: Older architecture — no FP8 support; Higher hourly cost than newer alternatives like L40S for inference; Approaching end-of-life as H100/H200 become standard. 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 A100 SXM4 80GB is a 6-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 A100 SXM4 80GB 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.