A30 vs RTX 4080

RTX 4080 demonstrates superior raw compute with 48.7 TFLOPS in both FP16 and FP32, over four times the A30's 10.3 TFLOPS per precision. This advantage accelerates model training by processing more operations per second and speeds inference for real-time applications. Workloads like fine-tuning transformers benefit most from this delta, as smaller batches complete epochs faster on RTX 4080.

A30 counters with 24 GB HBM2 VRAM exceeding RTX 4080's 16 GB GDDR6X, enabling larger batch sizes or bigger models without swapping to host memory. Its 933 GB/s bandwidth outperforms 717 GB/s on RTX 4080, reducing latency in memory-bound scenarios such as LLM inference with long contexts. Higher TDP on RTX 4080 at 320W versus 165W may limit density in power-constrained clouds.

Overall, compute-intensive tasks favor RTX 4080 for throughput, while memory constraints push toward A30 to maintain efficiency.

Select A30 for memory-intensive workloads exceeding 16 GB VRAM, such as inference on large language models with extensive context lengths. Its 24 GB HBM2 and 933 GB/s bandwidth support bigger batches without performance drops. Lower 165W TDP enables denser deployments in data centers, and NVLink facilitates multi-GPU setups for scaled scientific computing.

Opt for RTX 4080 in compute-bound tasks like model training or fine-tuning, where 48.7 TFLOPS FP16 delivers over 4x speedup versus A30's 10.3 TFLOPS. Cloud availability starts at $0.11 per hour, averaging $0.28 across eight providers. Newer Ada Lovelace architecture enhances efficiency for generative AI despite 320W TDP.