A100 SXM4 80GB vs RTX 3080

Memory capacity defines workload feasibility: the A100's 80 GB HBM2e VRAM handles massive models and large batch sizes, while the RTX 3080's 10-12 GB GDDR6X limits it to smaller datasets. Bandwidth reinforces this gap, as 2039 GB/s on the A100 sustains high-throughput data movement critical for training large neural networks, versus 760 GB/s on the RTX 3080 which bottlenecks intensive memory-bound operations.

FP16 performance favors the A100 at 312 TFLOPS for accelerated mixed-precision training and inference, enabling faster iterations on complex models; the RTX 3080's 29.8 TFLOPS suits lighter tasks but scales poorly. In FP32, the RTX 3080 matches at 29.8 TFLOPS against the A100's 19.5 TFLOPS, benefiting general-purpose computing, yet the A100's tensor cores optimize AI pipelines. Higher TDP of 400W on the A100 reflects sustained enterprise loads, contrasting the RTX 3080's 320W for bursty consumer use.

These specs translate to real-world impacts: A100 supports batch sizes up to 10x larger due to VRAM and bandwidth, reducing training epochs; RTX 3080 excels in inference for models under 10 GB where cost efficiency matters.

Select the A100 SXM4 80GB for large-scale AI training and inference requiring 80 GB VRAM, such as LLMs exceeding 10-12 GB model sizes. Its 2039 GB/s bandwidth and 312 TFLOPS FP16 enable efficient handling of massive datasets and multi-GPU clusters via NVLink, ideal for enterprise environments despite $1.33 per hour average cost.

Scientific simulations and HPC workloads benefit from InfiniBand support and SXM4 scalability, where the A100 outperforms in sustained 400W operations.

Opt for the RTX 3080 in budget-constrained scenarios like prototyping or small-scale inference, leveraging its low $0.13 per hour average pricing across cloud offers. With 10-12 GB VRAM and 760 GB/s bandwidth, it suffices for models fitting within these limits and balanced 29.8 TFLOPS FP16/FP32 for gaming or lightweight ML.

Consumer tasks such as Stable Diffusion generation or fine-tuning compact networks favor its 320W efficiency and PCIe simplicity without needing advanced interconnects.