A100 SXM4 40GB

The A100 SXM4 40GB 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 300W TDP, the A100 SXM4 40GB sits in the mid-range data center tier (300W), fitting standard GPU server form factors.

Memory capacity is 40GB of HBM2e with 1555 GB/s bandwidth. This determines which models can run without quantization: approximately 20B parameters at FP16 (2 bytes/param), 40B at INT8 (1 byte/param), or up to 80B 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 201 FLOP/byte (312 TFLOPS ÷ 1555 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 1555 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 40GB. 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 80GB of pooled VRAM — sufficient for 40B parameter models at FP16 — while a four-card setup reaches 160GB. 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 40GB are ML training, Multi-GPU NVLink setups, Research workloads. SXM4 form factor A100 40GB with NVLink 3.0. Lower VRAM than 80GB but full NVLink bandwidth for multi-GPU training. Key limitations to factor into your evaluation: Only 40GB VRAM — limits to ~30B models at FP16; No FP8 support; Higher cost than PCIe variant for inference-only workloads. 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 40GB 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 40GB 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.