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Rent GPUs for LLM training and fine-tuning

This page is a beginner guide for teams who want to teach a large language model using rented GPUs. A large language model (LLM) is a computer program that predicts text. Training means showing it huge amounts of text so it learns patterns. That is a different job from asking the model questions, which is called inference.

A GPU is a graphics processing unit: a chip that is very good at the math in training. GPU cloud means you rent those chips by the hour. Full training usually needs many GPUs working together. Fine-tuning (teaching an existing model a smaller job) is smaller and has its own page: GPU rental for model fine-tuning. Using the model to answer questions lives on GPU rental for LLM inference.

Who it is for: founders, research leads, and the person buying compute who has heard "we need H100s" and wants the plain-English version first. You do not need to be an ML engineer to follow this page.

You rent GPUs when you are building or heavily continuing a model, not just chatting with one. You turn them off when the run ends and you have a checkpoint (a saved snapshot of the model). If the same machines train around the clock for years, the next problem is usually electricity and a site. That is powered land.

What training actually is

Training is teaching. You feed the model examples. It guesses the next piece of text. You measure how wrong it was. You nudge millions of internal numbers (weights) so the next guess is slightly better. Repeat that billions of times.

That loop is why GPUs matter. Each step is a lot of the same kind of matrix math. GPUs do that math in parallel. A laptop GPU can teach a tiny model. A serious language model wants many data-center GPUs, a lot of memory on each chip, and fast links between those chips.

PyTorch is the common software library teams use to write this loop. NVIDIA NeMo is NVIDIA's framework for training and adapting language models on NVIDIA GPUs. Hugging Face Transformers is a widely used library of model code. You do not have to use any one of those. You do need a cloud image with NVIDIA drivers and CUDA, which is NVIDIA's software layer for NVIDIA chips.

You rent GPUs when you are still learning the size of the job

You rent GPUs when the model size is still moving, the dataset is still changing, and you do not know how many hours a full run will take. Paying a premium per hour to learn that is rational. Buying a hall of cards for a run that might fail in week two is not.

A typical rental training job looks like this: reserve several matching GPUs in one region, copy the dataset next to them, start the run, save checkpoints often, and shut the meter off when you have a result or a failure you can debug.

Ask for matching GPUs, not a mix of leftovers. Training code assumes the chips are the same. Ask how the GPUs in one server talk to each other. Slow links between chips make a "8-GPU" machine behave like a much weaker one. Ask where checkpoints go. If saving a snapshot has to cross the public internet, the run will stall.

Training is not inference, and it is not a small fine-tune

Three jobs get mixed up in procurement:

1. Training: teaching a large model from lots of data. Many GPUs. Long runs. This page. Continued training (teaching an existing model a large extra corpus) still belongs here.
2. Fine-tuning: teaching an existing model a smaller job, often with LoRA, which changes only extra weights. Days to a couple of weeks. See fine-tuning.
3. Inference: using a trained model to generate answers. See inference.

Do not train on text you are not allowed to use. Shared notebook GPUs are a poor place for a restricted corpus. Use a private network slice (a VPC) or single-tenant nodes when the data is sensitive. A demo notebook that "trains" for an hour is not a training plan.

What to check before you rent

Start with memory and count, not the famous product name. Can one GPU hold the piece of the model you intend to put on it? If not, you need more GPUs and a way for them to share work. Confirm CUDA drivers and that PyTorch or NeMo will actually run.

Ask about interconnect (fast links between GPUs), storage next to the chips, and preemption. Spot (interruptible) GPUs are cheaper and can vanish. They are fine if you checkpoint often. They are a bad fit for a deadline run you cannot replay. Egress is the fee for copying data out of the cloud. Moving a training set every experiment is how the bill leaves the GPU line.

GPU cloud partners (placeholder)

Partner rows below are placeholders until terms are signed. No live rates. No outbound URLs. When links go live, they will use rel="sponsored".

This table is not a ranking. It will reflect partner relationships once partners exist.

GPU cloud pricing varies. Verify live rates.

There is no honest single dollar-per-hour number on this page. List prices move with GPU type, region, and whether you pay only when the machine is on. Quoted "from" rates often skip storage, data transfer, idle reservations, and the engineer who keeps the run up. See the GPU cloud pricing index for how we talk about rates. We will not invent hourly prices here. Treat every public figure as stale until you re-check the provider.

Compare hours you will actually run. A training run that is "on" for three weeks is not the same product as a chatbot that answers all year.

When GPU rental stops working

Rental stops working when utilization is high and predictable, the hardware mix is stable, and the next constraint is megawatts and a site, not another login. A megawatt is a large unit of electrical power. You are then running a training cluster that happens to live on someone else's floor.

The honest alternative is powered land: a development site with a credible path to large-load electricity. Browse the U.S. data center directory, then read when GPU clusters need powered land. Hybrid is normal. Rent the experiments and the one-time pre-trains. Site the cluster that never turns off.

Find powered land when you need a site. Rent GPUs when you need capacity now.

Related

• GPU rental for LLM inference
• GPU rental for model fine-tuning
• When GPU clusters need powered land

FAQ

What is the difference between training and fine-tuning?

Training teaches a large model from a lot of data and usually needs many GPUs. Fine-tuning starts from a model that already knows language and teaches it a smaller job. Fine-tuning is the cheaper, shorter rental for most teams. See the fine-tuning guide.

Can I train an LLM on one rented GPU?

A small model, sometimes. A serious large language model, usually not. Memory on the chip runs out. You need several GPUs that can share the work. Ask the provider about multi-GPU nodes, not a single notebook card.

Should training run on spot GPUs?

Only if you save checkpoints often and can restart. Spot machines can disappear. Deadline training usually wants on-demand or dedicated nodes.

Do I need NVIDIA GPUs specifically?

Most of the common training stack (PyTorch, NeMo, many Hugging Face models) is built around NVIDIA's CUDA software. Other chips exist. This page talks about the NVIDIA path because that is what most GPU clouds rent today.

When is a dedicated cluster cheaper than renting for training?

When the same GPU shape would be busy most hours for a planning horizon in years, not for one run. Count real hours. We do not publish dollar-per-hour rates. Use the pricing index, then re-check the provider.

What if the limiter is power and a site, not another GPU?

Then rental is the wrong product. Use the powered-land GPU guide and what powered land is.

Sources

1. PyTorch
2. NVIDIA NeMo Framework overview
3. Hugging Face Transformers
4. NVIDIA CUDA Toolkit
5. What is powered land
6. U.S. data center directory
7. Powered Lands data center map

Need land or power instead? See the map or contact.