GPUs de Nuvem Legadas Pré-2020 — June 2026

What “release_year_max 2019” actually selects

Filtering to a maximum release year of 2019 narrows the list above to cloud GPUs whose silicon predates the modern AI boom — cards designed and launched before the 2020 wave of Ampere and the later HBM-heavy data-center parts. In practice this captures three NVIDIA generations that still circulate in rental fleets: Pascal (2016, e.g. the P100 and P40), Volta (2017, the V100), and Turing (2018–2019, the T4 and the RTX/Quadro consumer-derived cards). These are the GPUs that powered the first generation of large-scale deep learning, and they remain on the market because they are cheap, plentiful, and “good enough” for a surprising range of work.

The defining technical fault line for this era is the tensor core. Pascal has none; it does FP32 and FP16 on ordinary CUDA cores, so it predates the matrix-multiply acceleration that everything since assumes. Volta introduced first-generation tensor cores with FP16 accumulation. Turing added INT8 and INT4 tensor paths plus the T4’s strong inference focus. Nothing in this bucket supports BF16, FP8, or the structured sparsity features that arrived with Ampere and later — a distinction that matters a great deal when you map a 2024-era training recipe onto 2019 hardware.

What this hardware can and cannot do

The cards in this filter break into two practical roles.

• Datacenter HBM parts (P100, V100): these use HBM2 memory, giving the V100 roughly 16 GB or 32 GB at very high bandwidth (around 900 GB/s on the 32 GB SXM2 variant), with NVLink for multi-GPU scaling. The P100 sits lower, around 16 GB and ~700–730 GB/s. High bandwidth is the reason a Volta V100 still trains and fine-tunes mid-size models respectably despite its age.
• GDDR inference/utility parts (T4, P40, RTX-class): the T4 is a 70 W, single-slot card with 16 GB of GDDR6 (~320 GB/s) built for throughput inference and light training. The P40 carries 24 GB of GDDR5 but no tensor cores, making it a roomy-but-slow option for memory-hungry batch jobs.

For workloads, this generation genuinely fits:

• Inference and serving of small to mid-size models — classic CNNs, BERT-class transformers, embedding models, recommendation systems — where INT8 on Turing or FP16 on Volta is plenty.
• Fine-tuning and training of smaller models, especially when a 16–32 GB card with NVLink can be pooled across two or four GPUs.
• Rendering, simulation, and traditional HPC that depend on FP32/FP64 throughput rather than low-precision tensor math (the P100 and V100 retain meaningful FP64 performance).
• Learning, prototyping, and CI where cost per hour outweighs raw speed.

Where it falls short is modern large-model work. Running or training contemporary multi-billion-parameter language models is painful here: there is no BF16 or FP8, VRAM tops out at 32 GB per card, and quantization tricks that assume newer tensor formats may not accelerate. You can shard a large model across many V100s, but the interconnect and memory ceilings make it slow and operationally fiddly compared with a single newer card that simply holds the weights.

Rental economics of pre-2020 GPUs

The reason this filter exists is value. Because these chips are several generations old and abundant in the secondary and depreciated-fleet market, they occupy the budget end of the cost spectrum. They are typically the cheapest “real” accelerators a provider lists, well below current-generation data-center cards. Availability is usually good — these are rarely the scarce, waitlisted SKUs — and they show up frequently as spot or interruptible instances at steep discounts, which suits fault-tolerant batch inference and checkpointed training.

The trade-off is a throughput-per-dollar question rather than a sticker-price question. A newer GPU may cost several times more per hour yet finish a tensor-heavy job far faster, making it cheaper overall. So read the comparison above with your specific workload in mind: if the job is small, embarrassingly parallel, or latency-tolerant, legacy silicon often wins on total cost; if it is a large-model train that hammers tensor cores, the modern card usually wins despite the higher hourly rate. The table reflects live pricing and current stock, both of which move — use it, not a remembered figure, for the actual numbers.

What to check before renting from this tier

• VRAM per card and whether NVLink is exposed — a 32 GB NVLinked V100 pair behaves very differently from two isolated 16 GB cards.
• Supported precisions in your framework — confirm your stack falls back gracefully when BF16/FP8 are absent.
• Driver and CUDA compatibility — very old cards can hit CUDA compute-capability floors in the newest libraries.
• Spot interruption behavior and checkpointing — the discounts are real but so are the evictions.
• Storage and egress — on a cheap GPU, data-movement fees can quietly dominate the bill.

Frequently asked questions

Are pre-2020 cloud GPUs still worth renting?

Yes, for the right jobs. For inference, smaller-model fine-tuning, rendering, HPC, and cost-sensitive experimentation, Pascal/Volta/Turing parts deliver strong throughput per dollar. They are a poor fit for training or serving today’s largest models, where newer tensor formats and bigger VRAM matter more.

Do GPUs in the 2019-and-earlier filter have tensor cores?

It depends on the generation. Pascal (P100, P40) has none. Volta (V100) has first-generation tensor cores with FP16. Turing (T4 and RTX-class) adds INT8/INT4 paths. None of them support BF16 or FP8, which only arrived with later architectures.

Why are these GPUs usually cheaper to rent?

They are depreciated, widely deployed, and several generations behind current data-center hardware, so providers price them at the low end and frequently offer them as discounted spot or interruptible capacity. Just weigh the low hourly rate against slower completion times on tensor-heavy work.

Will my modern AI code run on a 2019-era GPU?

Often, but verify CUDA compute-capability and library support first, and expect to fall back from BF16/FP8 to FP16 or INT8. Memory ceilings of 16–32 GB per card may also force model sharding or quantization that newer single cards avoid.