Yes, for workloads that fit their memory, software, and latency requirements at a competitive total cost. AWS still offers NVIDIA T4-based G4dn instances for machine learning inference and small-scale training, including applications such as image classification and speech recognition. That is evidence of an ongoing use case, not proof that older GPUs are the cheapest choice for every model. Compare the cost of delivering the required throughput and response time, rather than hourly rental price alone.
How to read this finding
Large models, long contexts, or demanding response-time targets can favor newer systems. Software support can also constrain reuse: CUDA 13 removed offline compilation and library support for Maxwell, Pascal, and Volta architectures. NVIDIA says applications built with older toolkits can continue running on supported drivers; the change does not make those chips stop working. This index has no matched inference benchmark or total-cost dataset, so it cannot rank old and new GPUs by profitability or recommend a universal replacement date.
Company filings and hardware documentation reviewed October 11, 2026. Accounting examples use 2025 annual reports; vendor claims are attributed. Research citations do not measure profitability, and this index has no rental-price or resale-value series.
Benaich, Nathan. “Can older GPUs remain competitive for inference?” State of AI Report Compute Index. Web page updated 2026-10-11; data periods as specified above.
