When renting a GPU beats buying one
Renting a GPU can be better than buying when workloads are short, variable, or require capabilities beyond local hardware. Key factors include compute capability, inference performance, model memory, and software compatibility. The decision depends on duration, scale, and specific hardware requirements.
Compute capability and compatibility
A GPU's compute capability defines the hardware features and supported instructions for its architecture, as explained in NVIDIA's CUDA GPU list. When a local GPU lacks the necessary capability, renting a cloud instance with the appropriate compute capability can avoid the cost of upgrading hardware.
Compute capability numbers like 8.0, 8.6, or 9.0 indicate generations and feature sets. The NVIDIA page lists data center and workstation GPUs with their corresponding capability, so a developer can match a rental GPU to a required capability.
For example, a rented instance might provide an A100 with compute capability 8.0, which supports features older local cards do not. This allows testing with specific instruction sets without committing to a purchase.
Inference performance and benchmarking
The MLPerf Inference Datacenter benchmark suite measures how fast systems process inputs and produce results using trained models. Published results include systems that are available for purchase or rent, so a developer can compare rental options against local hardware.
The suite defines scenarios such as server and offline, with metrics like latency and throughput. Checking these results helps determine whether a rented GPU meets performance targets before a purchase is made.
Because MLPerf includes both Closed and Open divisions, one can see how different hardware and software stacks perform. This data is useful for choosing a rental that matches the desired performance level.
Memory requirements of large models
Large language models require substantial GPU memory. According to Hugging Face's optimization guide, loading a model with X billion parameters takes roughly 2 * X GB of VRAM in bfloat16 or float16 precision.
For example, a 70-billion-parameter model needs about 140 GB of VRAM, which exceeds the memory of a single consumer GPU. Renting a multi-GPU instance or a high-memory data center GPU can accommodate such models without buying expensive hardware.
Short input sequences are dominated by the weight memory; longer contexts multiply the requirement. Renting offers flexibility to scale memory up or down based on the model size.
Software installation and environment
PyTorch installation depends on the compute platform, such as CUDA version or CPU-only. The PyTorch local installation guide lets users choose a CUDA version and package manager, which simplifies setting up a compatible environment.
With a rented GPU, a developer can quickly configure a suitable CUDA version without modifying local hardware. The guide also lists support for Linux distributions and Python versions, so rental operating systems can be matched.
When a workload is temporary, renting avoids maintaining the software stack on permanent hardware. It also allows testing multiple CUDA versions on the same model to find the best performance.
Deciding between rent and buy
The choice depends on workload duration, hardware needs, and budget. The TOP500 list, which ranks supercomputers by High Performance Linpack (HPL) performance, illustrates the scale of high-end compute, as seen at the TOP500 site. For rare or extreme compute tasks, renting access to large-scale systems can be more practical than buying.
If a project runs for weeks or months with consistent hardware demands, buying may amortize costs. If the need is sporadic or varies in model size, renting allows scaling up and down.
Factors such as compute capability, inference performance, memory, and software compatibility should all be evaluated. A rental can be a test bed before making a purchase decision.
| Factor | Renting | Buying |
|---|---|---|
| Compute capability | Access to any capability without owning hardware | Limited to installed GPUs |
| Inference performance | Can match MLPerf results | Must benchmark locally |
| Memory | Scale to multi-GPU or high VRAM | Fixed memory per card |
| Software | Quickly switch CUDA versions | Stable environment |
| Cost | Pay per use | Upfront investment |
| Duration | Short or variable | Long-term consistent |
Frequently asked questions
What does compute capability tell about a GPU?
Compute capability defines the hardware features and supported instructions for each NVIDIA GPU architecture, as noted in NVIDIA's CUDA GPU list. It helps determine software compatibility.
How much VRAM does a 70-billion-parameter model need?
According to Hugging Face's guide, loading a model with X billion parameters requires about 2 * X GB of VRAM in bfloat16 or float16, so a 70B model needs roughly 140 GB.
Why are MLPerf Inference results useful when deciding to rent?
MLPerf Inference Datacenter lists results for systems that are available for purchase or rent, so you can compare performance before committing to hardware.
Can a user install PyTorch on a rented GPU with different CUDA versions?
Yes, PyTorch's installation guide lets you choose the CUDA version, so you can set up a compatible environment on a rented instance.
Is renting better for short-term AI projects?
If a workload is short or variable, renting avoids the upfront cost of hardware and allows scaling to larger GPUs when needed, as discussed in the article.