OVH GPUs vs Vultr: Short LLM Training Showdown

The first time we saw a GPU job on a public cloud provider complete in 15 minutes, only to have the setup and teardown process add another 20 minutes to the effective billing cycle, we knew we had a problem. Short, iterative LLM training runs—the bread and butter of experimentation—are a different beast than long, batch training jobs. The hourly rate of the GPU itself often becomes secondary to the friction of getting the job started, shipping data, and tearing it down cleanly. We needed to know which providers understood this workflow, and which were just selling raw compute by the clock.

The Hidden Costs of Iterative LLM Training

When you’re fine-tuning a Llama-3 8B model on a new dataset, you’re not looking for a multi-day training cluster. You’re looking for a quick feedback loop: spin up, run a few epochs, evaluate, tweak hyperparameters, spin down. Repeat this ten times a day, and suddenly the two-minute cold start or the five-minute driver installation becomes a significant portion of your total billed time. Add to that the overhead of data transfer, snapshotting, and the occasional instance launch failure, and your seemingly cheap $2/hour GPU can quickly become a $5/hour headache when amortized over effective training time. This is where many providers fall short, focusing on raw specs while ignoring the developer experience crucial for rapid iteration.

Our Testbed: What We Spun Up at OVH and Vultr

To keep the comparison fair and directly relevant to LLM fine-tuning, we focused on 40GB A100 instances, as this VRAM capacity is ample for many Llama-series 7B and 8B variants. We provisioned one instance from each provider in a Western Europe region (Frankfurt for Vultr, Gravelines for OVH) to minimize network latency between them and our local dev machines. Our workload was a LoRA fine-tune of Llama-3 8B using a 50,000-sample Alpaca-style dataset for 3 epochs. We tracked the time from API call to torch.cuda.is_available() (cold start), time to the first training epoch, and average tokens/second during training. For consistency, we followed our standard benchmarking methodology, installing Ubuntu 22.04, CUDA 12.3, PyTorch 2.2, and Hugging Face Transformers.

Provider	Instance SKU	GPU	VRAM	vCPU	RAM	Storage	Base $/hr
OVHcloud	gpu-nv-a100-8-40	A100	40 GB	8	40 GB	200 GB SSD	$1.92
Vultr	vcg-1-a100-40	A100	40 GB	16	128 GB	800 GB NVMe	$2.10

Note that Vultr’s instance offered significantly more vCPU and RAM, which might seem like an unfair advantage. However, for most LLM training, VRAM is the primary bottleneck, and CPU/RAM beyond a certain point often doesn’t scale training throughput significantly. We kept these instances active for several days each, simulating iterative runs, restarts, and snapshot operations.

OVH GPU Instances: Setup, Speed, and Surprises

Spinning up an OVH gpu-nv-a100-8-40 instance from their Public Cloud dashboard was straightforward, but the real work began after SSHing in. Unlike some providers that offer pre-baked GPU-ready images, OVH often requires manual driver installation. This isn’t inherently complex, but it’s a time sink for iterative workloads. Our initial setup, from instance launch to a working CUDA environment, took about 18 minutes. This included downloading the NVIDIA drivers, installing them, and setting up our Conda environment. Subsequent restarts were faster, but a fresh instance always incurred this overhead. This is a common pattern we’ve seen with OVH, even on their dedicated servers, as detailed in our Hetzner AX52 vs OVH Rise-3 comparison.

Once the environment was ready, the A100 performed as expected. Our Llama-3 8B LoRA fine-tune achieved an average of 3,850 tokens/second. Cold start, defined as the time from an API call to boot the instance to the first epoch starting (assuming a pre-configured image after the initial setup), averaged around 7 minutes. OVH’s egress fees are generally reasonable at $0.01/GB after a generous included allowance, which is a significant plus.

The main surprise was instance availability. While we eventually secured a 40GB A100, these instances can be spotty in certain regions, requiring persistence or trying different zones. For quick experiments, this can be a workflow killer, adding uncertainty to your iteration schedule.

Vultr Cloud GPU: Accessibility, Performance, and Pricing

Vultr’s experience was distinctly more polished for rapid deployment. They offer pre-configured marketplace images with NVIDIA drivers and CUDA pre-installed, often with popular ML frameworks ready to go. Launching a vcg-1-a100-40 and reaching a functional CUDA environment took us just 4 minutes using their PyTorch-ready image. This dramatically reduces the friction for iterative testing—you’re billed for the GPU, but you’re actually using it almost immediately.

Performance-wise, the 40GB A100 on Vultr delivered an average of 3,920 tokens/second during our Llama-3 8B fine-tune. This is marginally faster than OVH, likely due to the higher vCPU and RAM, but the difference is minimal for VRAM-bound tasks. The cold start time, from API call to first epoch, consistently hovered around 3 minutes. This speed-to-readiness is Vultr’s primary advantage for short runs. Their egress charges match OVH’s at $0.01/GB after a smaller 1TB free tier, which can be a factor if you’re pulling large datasets frequently. We explored these aspects in more detail in our full Vultr Cloud GPU review.

While Vultr’s hourly rate is slightly higher ($2.10/hr vs $1.92/hr), the reduced setup time means you often pay for less idle time. Their instance availability for A100s was also far more consistent in our testing period, which is crucial when you need a GPU now.

The Verdict: OVH vs Vultr for Your Next LLM Experiment

For the specific workflow of short, iterative LLM training runs, Vultr edges out OVH. The critical factor isn’t just the raw hourly rate, but the total time from “I need a GPU” to “my model is training.” Vultr’s pre-configured images and faster cold start times mean less wasted time and fewer surprises. While OVH offers a slightly lower hourly rate, the manual setup and potential availability issues can quickly negate those savings for developers who value rapid iteration.

Criterion	OVHcloud (A100 40GB)	Vultr (A100 40GB)
Base $/hr	$1.92	$2.10
Initial Setup Time	18 minutes	4 minutes
Cold Start (re-launch)	7 minutes	3 minutes
Llama-3 8B tokens/sec	3,850	3,920
Egress Overage	$0.01/GB (after 10TB)	$0.01/GB (after 1TB)
Ease of Use	Moderate (manual setup)	High (pre-configured images)
Availability	Variable	Consistent

If you’re a budget-conscious team already comfortable with manual server administration and willing to build and maintain your own GPU images, OVH still offers a compelling price point, especially if your runs are longer than an hour or two. However, for most ML engineers and researchers needing to quickly spin up an environment, run a short experiment, and then tear it down, Vultr’s operational efficiency makes it the clear winner. The extra $0.18/hour is a small price to pay for predictability and developer velocity. Just be mindful of egress fees on both platforms if your dataset is massive or you’re pulling models from external registries repeatedly. For us, Vultr gets the nod for our next LLM experiment, simply because it respects our time more.