Hosting an LLM Locally vs. Vast.ai: A Practical Comparison

As open-weight AI models have become increasingly capable, hosting an LLM yourself locally is an appealing option. But whether it's worth doing so is a whole other question. The answer depends on factors like what hardware you need, how often you'll use it, how much you're willing to spend upfront, and what your future plans entail.

An alternative option is to rent GPU capacity on demand and run models on Vast.ai instead.

In this post, we'll break down the advantages and trade-offs of hosting an LLM locally versus using Vast.ai, so you can choose the approach that best fits your needs.

Hosting an LLM on Your Own Hardware

Hosting an LLM locally means making a hardware investment upfront.

Depending on the machine you purchase—and due to extreme demand and supply shortages in the market today—that can involve spending several thousand dollars on a single GPU.

On the plus side, once you've purchased a GPU, you can use it whenever you want while remaining in full control of your data and networking environment. Your machine is often available, and you don't need to provision an instance every time you run your model.

However, hosting locally has some drawbacks, such as:

• High upfront costs for more powerful machines
• Limited VRAM that restricts which models you can run
• Hardware maintenance, cooling, and power consumption costs
• Having to upgrade when models and workloads get larger
• A single point of failure with nothing to fall back on

Local hosting gives you maximum control, but that comes with a commitment to the hardware you own. You're essentially paying for peak capacity even when your GPU sits idle.

And if your compute requirements change, your options are limited to what your existing machine can handle unless you have the budget to upgrade.

Running an LLM on Vast.ai

With Vast.ai, the steep upfront costs are eliminated. Instead of buying a GPU, you rent the compute you need on demand—from enterprise-grade GPUs like the NVIDIA A100 or H200, ultra-high-performance accelerators like the B200, top-tier consumer cards like the RTX 5090, and customizable, on-demand GPU/CPU clusters. You simply provision them when you need them, and you're never limited by the VRAM or performance of a machine you've purchased.

Getting an LLM up and running on Vast.ai is straightforward. Our Model Library features ready-to-run templates for open-weight models like Kimi K3, Gemma 4, Qwen3.8, and more. Just select an inference engine template like Ollama + WebUI or vLLM, choose a GPU with enough disk space and VRAM for the model you want to run, and then launch your instance.

Renting GPU compute on Vast.ai also means you pay for GPU time while the instance is running. When you stop an instance, GPU charges stop, although storage charges continue until you destroy the instance. If your compute requirements change, you can choose a different machine the next time you deploy.

This makes Vast.ai a particularly useful option for workloads that vary over time. Bursty and unpredictable? Not a problem. You can experiment with different models and hardware, scale up when you need more compute, and avoid making a large upfront investment in a GPU that may eventually become insufficient for your needs.

The trade-off: your ongoing costs depend on how much compute you need and how often you use it.

Local Hosting vs. Vast.ai: Comparison at a Glance

Here's a quick overview of factors to consider when deciding between local hosting and cloud GPU rental on Vast.ai:

The biggest difference is flexibility. With local hosting, you invest in the hardware upfront and get consistent access to it afterward. With Vast.ai, you trade ownership for the flexibility to match your compute to what you actually need at any given time, paying for the resources you use.

Which Option Makes More Sense for You?

The right option depends primarily on how you plan to use your LLM.

Local hosting may be the better fit if you:

• Have predictable, sustained workloads that justify investing in dedicated hardware.
• Want complete control over your hardware, data, and networking environment.
• Already have a GPU capable of running the models you intend to use or have the budget to buy one outright.

Vast.ai may make more sense if you:

• Have variable or bursty compute needs that are likely to change over time.
• Want to choose from different GPU configurations depending on the model you're running.
• Prefer to experiment with different models or hardware without making a long-term investment.

Security and compliance are also important considerations. With locally owned hardware, you have direct control over where your data is stored and how your infrastructure is secured.

With Vast.ai, workloads run in isolated containers, keeping each client's workload separate from other tenants. For workloads with stricter requirements, Vast.ai's Secure Cloud provides access to machines hosted in vetted data centers. You should still evaluate the selected offer and your own configuration against the requirements of your workload.

The Bottom Line

The right choice comes down to the amount of flexibility you need—but the two options aren't mutually exclusive. You could run smaller models locally and use Vast.ai when you need more VRAM or additional capacity. Renting GPU compute can give you a way to extend your local setup without replacing your existing hardware.

If you prefer not to make a large upfront investment, Vast.ai can also reduce compute costs. On average, Vast.ai users save 5–6x on GPU compute compared with traditional cloud providers.

Browse available GPU instances on Vast.ai and find the compute that fits your LLM today.